Archives of Computational Methods in Engineering最新文献

{"title":"Differential Evolution: A Survey on Their Operators and Variants","authors":"Elivier Reyes-Davila,&nbsp;Eduardo H. Haro,&nbsp;Angel Casas-Ordaz,&nbsp;Diego Oliva,&nbsp;Omar Avalos","doi":"10.1007/s11831-024-10136-0","DOIUrl":"10.1007/s11831-024-10136-0","url":null,"abstract":"<div><p>The Differential Evolution (DE) algorithm is one of the most popular and studied approaches in Evolutionary Computation (EC). Its simple but efficient design, such as its competitive performance for many real-world optimization problems, has positioned it as the standard comparison scheme for any proposal in the field. Precisely, its simplicity has allowed the publication of a great number of variants and improvements since its inception in 1997. Moreover, several DE variants are recognized as well-founded and highly competitive algorithms in the literature. In addition, the multiple DE applications and their proposed modifications in the state-of-the-art have propitiated the drafting of many review and survey works. However, none of the DE compilation work has studied the different variants of DE operators exclusively, which would benefit future DE enhancements and other topics. Therefore, in this work, a survey analysis of the variants of DE operators is presented. This study focuses on the proposed DE operators and their impact on the EC literature over the years. The analysis allows understanding of each year’s trends, the improvements that marked a milestone in the DE research, and the feasible future directions of the algorithm. Finally, the results show a downward trend for mutation or crossover variants while readers are increasingly interested in initialization and selection enhancements.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 1","pages":"83 - 112"},"PeriodicalIF":9.7,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141105087","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":"Lung Cancer Detection Systems Applied to Medical Images: A State-of-the-Art Survey","authors":"Sher Lyn Tan,&nbsp;Ganeshsree Selvachandran,&nbsp;Raveendran Paramesran,&nbsp;Weiping Ding","doi":"10.1007/s11831-024-10141-3","DOIUrl":"10.1007/s11831-024-10141-3","url":null,"abstract":"<div><p>Lung cancer represents a significant global health challenge, transcending demographic boundaries of age, gender, and ethnicity. Timely detection stands as a pivotal factor for enhancing both survival rates and post-diagnosis quality of life. Artificial intelligence (AI) emerges as a transformative force with the potential to substantially enhance the accuracy and efficiency of Computer-Aided Diagnosis (CAD) systems for lung cancer. Despite the burgeoning interest, a notable gap persists in the literature concerning comprehensive reviews that delve into the intricate design and architectural facets of these systems. While existing reviews furnish valuable insights into result summaries and model attributes, a glaring absence prevails in offering a reliable roadmap to guide researchers towards optimal research directions. Addressing this gap in automated lung cancer detection within medical imaging, this survey adopts a focused approach, specifically targeting innovative models tailored solely for medical image analysis. The survey endeavors to meticulously scrutinize and merge knowledge pertaining to both the architectural components and intended functionalities of these models. In adherence to PRISMA guidelines, this survey systematically incorporates and analyzes 119 original articles spanning the years 2019–2023 sourced from Scopus and WoS-indexed repositories. The survey is underpinned by three primary areas of inquiry: the application of AI within CAD systems, the intricacies of model architectural designs, and comparative analyses of the latest advancements in lung cancer detection systems. To ensure coherence and depth in analysis, the surveyed methodologies are categorically classified into seven distinct groups based on their foundational models. Furthermore, the survey conducts a rigorous review of references and discerns trend observations concerning model designs and associated tasks. Beyond synthesizing existing knowledge, this survey serves as a guide that highlights potential avenues for further research within this critical domain. By providing comprehensive insights and facilitating informed decision-making, this survey aims to contribute to the body of knowledge in the study of automated lung cancer detection and propel advancements in the field.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 1","pages":"343 - 380"},"PeriodicalIF":9.7,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11831-024-10141-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141110519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Systematic Review on Generative Adversarial Network (GAN): Challenges and Future Directions","authors":"Ankitha A. Nayak,&nbsp;P. S. Venugopala,&nbsp;B. Ashwini","doi":"10.1007/s11831-024-10119-1","DOIUrl":"10.1007/s11831-024-10119-1","url":null,"abstract":"<div><p>Generative adversarial network, in short GAN, is a new convolution neural network (CNN) based framework with the great potential to determine high dimensional data from its feedback. It is a generative model built using two CNN blocks named generator and discriminator. GAN is a recent and trending innovation in CNN with evident progress in applications like computer vision, cyber security, medical and many more. This paper presents a complete overview of GAN with its structure, variants, application and current existing work. Our primary focus is to review the growth of GAN in the computer vision domain, specifically on image enhancement techniques. In this paper, the review is carried out in a funnel approach, starting with a broad view of GAN in all domains and then narrowing down to GAN in computer vision and, finally, GAN in image enhancement. Since GAN has cleverly acquired its position in various disciplines, we are showing a comparative analysis of GAN v/s ML v/s MATLAB computer vision methods concerning image enhancement techniques in existing work. The primary objective of the paper is to showcase the systematic literature survey and execute a comparative analysis of GAN with various existing research works in different domains and understand how GAN is a better approach compared to existing models using PRISMA guidelines. In this paper, we have also studied the current GAN model for image enhancement techniques and compared it with other methods concerning PSNR and SSIM.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 8","pages":"4739 - 4772"},"PeriodicalIF":9.7,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140938514","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 Study of Deep Learning Methods for Kidney Tumor, Cyst, and Stone Diagnostics and Detection Using CT Images","authors":"Yogesh Kumar,&nbsp;Tejinder Pal Singh Brar,&nbsp;Chhinder Kaur,&nbsp;Chamkaur Singh","doi":"10.1007/s11831-024-10112-8","DOIUrl":"10.1007/s11831-024-10112-8","url":null,"abstract":"<div><p>Kidney disease affects millions worldwide which emphasizes the need for early detection. Recent advancements in deep learning have transformed medical diagnostics and provide promising solutions to detect various kidney diseases. This paper aims to develop a reliable AI based learning system for effective prediction and classification of kidney diseases. The research involves a dataset of 12,446 kidney images which include cysts, tumor, stones, and healthy samples. The data undergoes thorough preprocessing to eliminate noise and enhance the quality of image. Segmentation techniques like Otsu’s binarization, Distance transform, and watershed transformation are applied to accurately delineate and identify distinct regions of interest followed by contour feature extraction which includes parameters like area, intensity, width, height, etc. Subsequently, different deep learning models such as DenseNet201, EfficientNetB0, InceptionResNetV2, MobileNetv2, ResNet50V2, and Xception are trained on incorporating with three optimizers—RMSprop, SGD, as well as Adam and are examined for the metrics such as accuracy, loss, precision, recall, RMSE, and F1 score. Notably, the Xception model outperformed others by achieving an accuracy of 99.89% with RMSprop. Similarly, ResNet50V2 and DenseNet201 demonstrated impressive accuracy of 99.68% with SGD and Adam optimizers respectively. These findings highlight the effectiveness of AI and deep transfer learning in accurate and effective kidney disease detection as well as classification.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 7","pages":"4163 - 4188"},"PeriodicalIF":9.7,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140938596","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":"Artificial Intelligence Techniques for the Photovoltaic System: A Systematic Review and Analysis for Evaluation and Benchmarking","authors":"Abhishek Kumar,&nbsp;Ashutosh Kumar Dubey,&nbsp;Isaac Segovia Ramírez,&nbsp;Alba Muñoz del Río,&nbsp;Fausto Pedro García Márquez","doi":"10.1007/s11831-024-10125-3","DOIUrl":"10.1007/s11831-024-10125-3","url":null,"abstract":"<div><p>Novel algorithms and techniques are being developed for design, forecasting and maintenance in photovoltaic due to high computational costs and volume of data. Machine Learning, artificial intelligence techniques and algorithms provide automated, intelligent and history-based solutions for complex scenarios. This paper aims to identify through a systematic review and analysis the role of artificial intelligence algorithms in photovoltaic systems analysis and control. The main novelty of this work is the exploration of methodological insights in three different ways. The first approach is to investigate the applicability of artificial intelligence techniques in photovoltaic systems. The second approach is the computational study and analysis of data operations, failure predictors, maintenance assessment, safety response, photovoltaic installation issues, intelligent monitoring etc. All these factors are discussed along with the results after applying the artificial intelligence techniques on photovoltaic systems, exploring the challenges and limitations considering a wide variety of latest related manuscripts.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 8","pages":"4429 - 4453"},"PeriodicalIF":9.7,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11831-024-10125-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140938313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comprehensive Review of Bias in Deep Learning Models: Methods, Impacts, and Future Directions","authors":"Milind Shah,&nbsp;Nitesh Sureja","doi":"10.1007/s11831-024-10134-2","DOIUrl":"10.1007/s11831-024-10134-2","url":null,"abstract":"<div><p>This comprehensive review and analysis delve into the intricate facets of bias within the realm of deep learning. As artificial intelligence and machine learning technologies become increasingly integrated into our lives, understanding and mitigating bias in these systems is of paramount importance. This paper scrutinizes the multifaceted nature of bias, encompassing data bias, algorithmic bias, and societal bias, and explores the interconnectedness among these dimensions. Through an exploration of existing literature and recent advancements in the field, this paper offers a critical assessment of various bias mitigation techniques. It examines the challenges faced in addressing bias and emphasizes the need for an intersectional and inclusive approach to effectively rectify disparities. Furthermore, this review underscores the importance of ethical considerations in the development and deployment of deep learning models. It highlights the necessity of diverse representation in data, fairness-aware algorithms, and interpretability as key elements in creating bias-free AI systems. By synthesizing existing research and providing a holistic overview of bias in deep learning, this paper aims to contribute to the ongoing discourse on mitigating bias and fostering equity in artificial intelligence systems. The insights presented herein can serve as a foundation for future research and as a guide for practitioners, policymakers, and stakeholders to navigate the complex landscape of bias in deep learning.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 1","pages":"255 - 267"},"PeriodicalIF":9.7,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140938682","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 Biomaterials, Biomechanics/Mechanobiology, and Biofabrication: State of the Art and Perspective","authors":"Chi Wu,&nbsp;Yanan Xu,&nbsp;Jianguang Fang,&nbsp;Qing Li","doi":"10.1007/s11831-024-10100-y","DOIUrl":"10.1007/s11831-024-10100-y","url":null,"abstract":"<div><p>In the past three decades, biomedical engineering has emerged as a significant and rapidly growing field across various disciplines. From an engineering perspective, biomaterials, biomechanics, and biofabrication play pivotal roles in interacting with targeted living biological systems for diverse therapeutic purposes. In this context, in silico modelling stands out as an effective and efficient alternative for investigating complex interactive responses in vivo. This paper offers a comprehensive review of the swiftly expanding field of machine learning (ML) techniques, empowering biomedical engineering to develop cutting-edge treatments for addressing healthcare challenges. The review categorically outlines different types of ML algorithms. It proceeds by first assessing their applications in biomaterials, covering such aspects as data mining/processing, digital twins, and data-driven design. Subsequently, ML approaches are scrutinised for the studies on mono-/multi-scale biomechanics and mechanobiology. Finally, the review extends to ML techniques in bioprinting and biomanufacturing, encompassing design optimisation and in situ monitoring. Furthermore, the paper presents typical ML-based applications in implantable devices, including tissue scaffolds, orthopaedic implants, and arterial stents. Finally, the challenges and perspectives are illuminated, providing insights for academia, industry, and biomedical professionals to further develop and apply ML strategies in future studies.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 7","pages":"3699 - 3765"},"PeriodicalIF":9.7,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11831-024-10100-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140886834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive Review on Seismic Pounding Between Adjacent Buildings and Available Mitigation Measures","authors":"Ahmed Elgammal,&nbsp;Ayman Seleemah,&nbsp;Mohammed Elsharkawy,&nbsp;Hytham Elwardany","doi":"10.1007/s11831-024-10114-6","DOIUrl":"10.1007/s11831-024-10114-6","url":null,"abstract":"<div><p>Seismic pounding has taken place in several earthquake events since adjacent structures that lack adequate separation distance usually suffer from repetitive, severe collisions. These collisions result in considerable impact forces in addition to acceleration spikes, thus dealing damage to both structural and non-structural elements. So, a meaningful effort has been widely directed towards the investigation of that phenomenon, leading to a considerable number of publications that are related to that field of study. A review of these publications has thus become a matter of interest. Accordingly, this paper mainly aims to present a detailed state-of-the-art review concerned with seismic pounding between adjacent buildings. Firstly, general definitions, types, and causes of seismic pounding are addressed. Later, facts and statistics of historical earthquake incidents that reflect the scale of the threat caused by seismic pounding are clarified. Moreover, the effect of seismic pounding on fixed-base and base-isolated buildings is discussed. Furthermore, the effect of soil-structure interaction is also presented. Additionally, alternative mitigation methods for seismic pounding are presented. Their classification, types, efficiency, and applicability are also discussed. Eventually, different impact analytical models that can be used to simulate seismic pounding in theoretical studies are discussed. By the end of this paper, deficiencies in previous studies are clarified in order to be taken into account throughout future studies.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 7","pages":"4269 - 4304"},"PeriodicalIF":9.7,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11831-024-10114-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140836005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of Photoelectric Conversion Technology in Photoelectric Signal Sampling System","authors":"Guobin Zhao,&nbsp;Hui Zhao,&nbsp;Jian Zhang,&nbsp;Chong Chen,&nbsp;Wang Tao","doi":"10.1007/s11831-024-10133-3","DOIUrl":"10.1007/s11831-024-10133-3","url":null,"abstract":"<div><p>The objective of this study is to investigate the use of photoelectric conversion technology in the process of creating enhanced photoelectric signal sampling systems using photoelectric conversion technology. The purpose of this research is to strengthen the sensitivity and dependability of signal capture by focusing on three primary aspects: the improvement of absorption, the downsizing of the device, and the efficiency of the conversion. It is possible to increase the amount of light that is absorbed by using localized surface Plasmon resonance for the goal of absorption augmentation. This, in turn, allows for an expansion of the spectrum of signals that may be detected. When compared to other components, the miniaturization component provides a greater emphasis on the design of small devices. This facilitates integration into small-scale sensors and systems, which ultimately results in improved mobility and flexibility. The author of the book “Conversion Efficiency” explores the benefits of plasmonic effects pertaining to the enhancement of photoelectric conversion, which finally leads to an improvement in the performance of the system. The use of photoelectric conversion technology has significant repercussions for a broad variety of applications, some of which include sensing, communication, and instrumentation, amongst others. The promise that this method will bring about a revolution in the processes of data collection is that it will make it possible to provide signal sampling devices that are exceedingly sensitive, compact, and efficient. It is clear that the findings of this research provide a significant contribution to the advancement of technology in domains where precision and reliability are of the highest significance. In addition, this discovery constitutes a significant advancement in the development of methods for sampling photoelectric signals.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 1","pages":"233 - 253"},"PeriodicalIF":9.7,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140835986","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 Comparative Analysis of Transient Finite-Strain Coupled Diffusion-Deformation Theories for Hydrogels","authors":"Jorge-Humberto Urrea-Quintero,&nbsp;Michele Marino,&nbsp;Thomas Wick,&nbsp;Udo Nackenhorst","doi":"10.1007/s11831-024-10101-x","DOIUrl":"10.1007/s11831-024-10101-x","url":null,"abstract":"<div><p>This work presents a comparative review and classification between some well-known thermodynamically consistent models of hydrogel behavior in a large deformation setting, specifically focusing on solvent absorption/desorption and its impact on mechanical deformation and network swelling. The proposed discussion addresses formulation aspects, general mathematical classification of the governing equations, and numerical implementation issues based on the finite element method. The theories are presented in a unified framework demonstrating that, despite not being evident in some cases, all of them follow equivalent thermodynamic arguments. A detailed computational analysis is carried out where Taylor–Hood elements are employed in the spatial discretization to satisfy the inf-sup condition and to prevent spurious numerical oscillations. The resulting discrete problems are solved using the FEniCS platform through consistent variational formulations, employing both monolithic and staggered approaches. We conduct benchmark tests on various hydrogel structures, demonstrating that major differences arise from the chosen volumetric response of the hydrogel. The significance of this choice is frequently underestimated in the state-of-the-art literature but has been shown to have substantial implications on the resulting hydrogel behavior.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 7","pages":"3767 - 3800"},"PeriodicalIF":9.7,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11831-024-10101-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140836010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}