Archives of Computational Methods in Engineering最新文献

{"title":"Concept, Creation, Services and Future Directions of Digital Twins in the Construction Industry: A Systematic Literature Review","authors":"Jiming Liu,&nbsp;Liping Duan,&nbsp;Siwei Lin,&nbsp;Ji Miao,&nbsp;Jincheng Zhao","doi":"10.1007/s11831-024-10140-4","DOIUrl":"10.1007/s11831-024-10140-4","url":null,"abstract":"<div><p>Currently, the engineering problems encountered in digital transformation of the construction industry are very complicated and need to be solved by integrating multiple technologies. Consequently, the concept of digital twin (DT) was introduced and quickly applied throughout the building lifecycle. Despite this, many practitioners lack understanding of DT in the construction industry (DT-CI) and its implementation. To overcome this issue, this paper presents a comprehensive and detailed review of DT-CI through a systematic literature review (SLR) that incorporates both quantitative and qualitative analysis. In this study, 222 DT-CI studies were selected from a pool of 2619 publications across multiple databases, and 43 related researches were supplemented by the backward snowballing method based on co-citation analysis to generate the final bibliographic database. This paper quantitatively analyzes the current state, hotspots, and development trends of DT-CI research through a bibliometric review, and systematically clarifies the concept, creation, services, and future directions of DT-CI through a framework-based review. Finally, based on the SLR outcomes, this paper offers recommendations for future work and DT-CI implementation. Contrary to other reviews within this field, this paper adheres to a rigorous SLR protocol to ensure the reproducibility of review results. Moreover, by comparing construction and non-construction DT concepts, we highlight the unique characteristics of DT-CI, namely its association with building information modeling (BIM) and emphasis on geometric reconstruction of building entities.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 1","pages":"319 - 342"},"PeriodicalIF":9.7,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141105933","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":"Comprehensive Review on MRI-Based Brain Tumor Segmentation: A Comparative Study from 2017 Onwards","authors":"Amit Verma,&nbsp;Shiv Naresh Shivhare,&nbsp;Shailendra P. Singh,&nbsp;Naween Kumar,&nbsp;Anand Nayyar","doi":"10.1007/s11831-024-10128-0","DOIUrl":"10.1007/s11831-024-10128-0","url":null,"abstract":"<div><p>Brain tumor segmentation has been a challenging and popular research problem in the area of medical imaging and computer-aided diagnosis. In the last few years, especially since 2017, researchers have significantly contributed for solving and enhancing the performance of brain tumor abnormality detection and tumor segmentation from magnetic resonance (MR) images. This paper presents a detailed and intensive review of automated brain disease diagnosis and tumor segmentation methods obtained by investigating numerous recent articles. In the first phase, an extensive literature search is conducted with more than 600 articles from medical image analysis, brain disease diagnosis, and tumor segmentation. Around 50% of articles are removed after initial scanning based on certain criteria, i.e., publication year, number of citations, and bibliographic indexing. A total of 161 relevant articles are finally selected in the second phase based on their performance and novelty of the proposed methods. Furthermore, the selected articles are investigated from the perspectives of methodology and performance. Overall methods exploited for brain disease detection and tumor segmentation are categorised into three broad classes, i.e., conventional methods, machine learning-based methods, and deep learning-based methods. As deep learning-based methods are state-of-the-art for computer-aided diagnosis (CAD) nowadays, we investigated several deep learning models, such as the convolutional neural network (CNN), the generative adversarial network (GAN), the U-Net, etc., along with residual block and attention gate, with respect to their learning mechanisms and hyper-parameter tuning. Methods from each class are rigorously reviewed and summarised by identifying their advantages, disadvantages, dataset, MR modality used, and type of images (2D/3D) processed. The methods are also analysed and compared based on their performance in various measures such as dice similarity coefficient (DSC), sensitivity, positive predictive value (PPV), Specificity, Jaccard Index (JI), Accuracy, Hausdorff distance, and computation time. In this review, the high heterogeneity of articles based on different methodologies is considered in light of the recent progress and development of brain tumor detection and segmentation. During analysis, it has been observed that deep learning-based methods, especially various variants of the U-Net model, outperform other approaches for brain tumor segmentation.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 8","pages":"4805 - 4851"},"PeriodicalIF":9.7,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141149670","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 Scoping Review on Simulation-Based Design Optimization in Marine Engineering: Trends, Best Practices, and Gaps","authors":"Andrea Serani,&nbsp;Thomas P. Scholcz,&nbsp;Valentina Vanzi","doi":"10.1007/s11831-024-10127-1","DOIUrl":"10.1007/s11831-024-10127-1","url":null,"abstract":"<div><p>This scoping review assesses the current use of simulation-based design optimization (SBDO) in marine engineering, focusing on identifying research trends, methodologies, and application areas. Analyzing 277 studies from Scopus and Web of Science, the review finds that SBDO is predominantly applied to optimizing marine vessel hulls, including both surface and underwater types, and extends to key components like bows, sterns, propellers, and fins. It also covers marine structures and renewable energy systems. A notable trend is the preference for deterministic single-objective optimization methods, indicating potential growth areas in multi-objective and stochastic approaches. The review points out the necessity of integrating more comprehensive multidisciplinary optimization methods to address the complex challenges in marine environments. Despite the extensive application of SBDO in marine engineering, there remains a need for enhancing the methodologies’ efficiency and robustness. This review offers a critical overview of SBDO’s role in marine engineering and highlights opportunities for future research to advance the field.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 8","pages":"4709 - 4737"},"PeriodicalIF":9.7,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11831-024-10127-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141060862","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 Review on Beamforming Optimization Techniques for IRS assisted Energy Harvesting","authors":"Pradeep Vishwakarma,&nbsp;Dipanjan Bhattacharjee,&nbsp;Sourav Dhar,&nbsp;Samarendra Nath Sur","doi":"10.1007/s11831-024-10118-2","DOIUrl":"10.1007/s11831-024-10118-2","url":null,"abstract":"<div><p>Intelligent reflecting surfaces (IRS) recently gained prominence due to their ability to adapt and tweak their configuration in real-time to create an intelligent wireless environment. Hence, it can elevate wireless connectivity, signal strength, data rate, coverage, and mitigate signal blockage or interference in future wireless networks. A comprehensive review of IRSs has been conveyed in this paper, emphasizing beamforming optimization strategies in the realm of energy harvesting with IRS assistance. The discussion encompasses an overview of IRS hardware design, practical IRS prototypes for hardware design, a summary of related works, and an equivalent RLC circuit model. Additionally, an extensive comparative analysis of IRS architecture, shape, size, advantages, drawbacks, and applications is presented, considering existing research. Further, the paper examines the most pivotal cost and economic aspects of IRS to optimize energy harvesting and coverage enhancement. The paper explores beamforming techniques and examines various optimization methods aimed at maximizing the potential of IRS for energy harvesting. Furthermore, the paper delves into the wide range of potential applications that IRS-assisted wireless communication networks can offer. Despite the significant promises of IRS technology, it faces substantial research challenges in optimization. This paper addresses and highlights these challenges and limitations associated with the IRS, paving the way for future research directions.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 8","pages":"4359 - 4427"},"PeriodicalIF":9.7,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140938509","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":"Evolution of Quantum Cryptography in Response to the Computational Power of Quantum Computers: An Archival View","authors":"Priya Sharma,&nbsp;Vrinda Gupta,&nbsp;Sandeep Kumar Sood","doi":"10.1007/s11831-024-10122-6","DOIUrl":"10.1007/s11831-024-10122-6","url":null,"abstract":"<div><p>Quantum cryptography (QC), rooted in the principles of quantum mechanics, stands as a beacon of security, offering an unparalleled level of protection against quantum attacks. This exceptional attribute has spurred researchers from diverse scientific disciplines to actively collaborate in advancing the field toward practical implementation. Physicists, computer scientists, engineers, and mathematicians are collectively channeling their efforts, leading to a substantial body of research outcomes. Hence, through this study, we delve into comprehending the multidisciplinary research landscape of QC through scientometrics. Here, we analyze the research outcomes in QC to discern its pattern in terms of publications and citations. Additionally, we identify the most influential countries, authors, and communication sources contributing to various facets of QC. Furthermore, this study also provides a research trajectory that outlines the prevalent research themes and current areas of research in QC. This information serves as a guiding light for newcomers, offering them direction and insight into the dynamic field of QC.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 8","pages":"4533 - 4555"},"PeriodicalIF":9.7,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140886956","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}