Archives of Computational Methods in Engineering最新文献

{"title":"Deep Learning Models for Skin Cancer Classification Across Diverse Color Spaces: Comprehensive Analysis","authors":"Anisha Paul,&nbsp;Asfak Ali,&nbsp;Sheli Sinha Chaudhuri","doi":"10.1007/s11831-024-10160-0","DOIUrl":"10.1007/s11831-024-10160-0","url":null,"abstract":"<div><p>Color space plays an important role in various aspects of imaging tasks. However, in deep learning-based computer vision, the RGB color model is predominantly employed. This research analyzes the impact of deep convolutional neural networks on cancer classification across different color spaces. The five most popular deep learning models undergo training and testing in eleven color spaces, revealing that YUV, LAB, and YIQ consistently outperform other color models in most cases. RGB images are frequently converted to alternative color spaces for enhanced representation in specific applications, like object detection and segmentation. This transformation induces alterations in the features of the color image due to variations in pixel intensity information across different color models. In this research, the aforementioned principle is applied to the classification of skin cancer using deep learning networks on images of skin lesions. The results exhibit diverse responses, with some networks achieving higher accuracy in alternative color spaces compared to RGB, while others do not. This study provides insights into the classification performance across RGB, HED, HSV, LAB, RGBCIE, XYZ, YCbCr, YDbDr, YIQ, YPbPr, and YUV color spaces. The research aims to illustrate how deep learning facilitates the analysis of skin cancer images in different color spaces.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 8","pages":"4455 - 4483"},"PeriodicalIF":9.7,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141568125","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":"Correction: Machine-Learning Methods for Estimating Performance of Structural Concrete Members Reinforced with Fiber-Reinforced Polymers","authors":"Farzin Kazemi, N. Asgarkhani, Torkan Shafighfard, R. Jankowski, Doo-Yeol Yoo","doi":"10.1007/s11831-024-10161-z","DOIUrl":"https://doi.org/10.1007/s11831-024-10161-z","url":null,"abstract":"","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":" 5","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141668551","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":"State-of-the-Art Review on Determining One-Dimensional Consolidation Parameters Based on Compression and Distribution of Pore Water Pressure: Coefficient of Consolidation (cv), End of Primary (EOP) Consolidation","authors":"Bartłomiej Szczepan Olek","doi":"10.1007/s11831-024-10154-y","DOIUrl":"https://doi.org/10.1007/s11831-024-10154-y","url":null,"abstract":"<p>Predicting the time rate of consolidation is one of the major aspects of structure design, founded on compressible fine-grained soil. The time to achieve the required advancement of the consolidation process is proportional to the coefficient of consolidation (<i>c</i>_<i>v</i>). In practical applications, the settlement rate is directly related to the excess pore water pressure dissipation rate. A plethora of interpretation methods have been proposed for determining consolidation parameters from laboratory one-dimensional consolidation test in the past decades. This state-of-the-art review presents a comprehensive literature study of available approaches for establishing both coefficient of consolidation and end of primary (EOP) consolidation using compression and pore water pressure laboratory data. The classification of the methods has been made to set in order interpretation approaches for future selection and comparisons. The first part of the paper describes approaches based on graphical curve-fitting. This part includes five approaches: square root of time fitting approach, Semi-logarithmic fitting approach, Differential methods, Hyperbolic approach, and approach based on excess pore water pressure dissipation. In addition, a method comparison study has been performed to evaluate the degree of agreement between selected methods statistically. For this purpose, simple regression and Bland &amp; Altman differences analysis have been used. The second part refers to the computational-based approach, covering a wide range of methods centred on full-matching treated by least-squares, correlational equations linking <i>c</i>_<i>v</i> with index properties and soft computing approaches. A thorough insight into recently published literature on machine learning and physics-informed deep learning incorporated to derive the representative value of <i>c</i>_<i>v</i> has also been compiled.</p>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"28 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141548659","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":"Static Modal Analysis: A Review of Static Structural Analysis Methods Through a New Modal Paradigm","authors":"Jonas Feron,&nbsp;Pierre Latteur,&nbsp;João Pacheco de Almeida","doi":"10.1007/s11831-024-10082-x","DOIUrl":"10.1007/s11831-024-10082-x","url":null,"abstract":"<div><p>This article is a state-of-art review on static structural computations for pin-jointed structures, revising the last forty years of scientific research on the subject matter through the introduction of <i>static modal analysis</i>. This novel paradigm is inspired by the so-called singular value decomposition (SVD) of the equilibrium matrix and by dynamic modal analysis. In dynamics, modal analysis requires the solution of an eigenvalue problem, which returns the natural frequencies of the structure and the corresponding mode shapes of vibration, the eigenvectors. The application of the static modal analysis to the four types of linear trusses—determinate or indeterminate from the static and kinematic viewpoints—allows re-interpreting the well-known force method and displacement method of structural analysis. Central to this proposal is the solution of static equilibrium and compatibility equations in a modal space where the relations between the extensional, inextensional, and self-stress modes are unequivocally identified. Their physical interpretation, also at the equilibrium and compatibility levels, is discussed and illustrated by key accompanying examples of structures subjected to external loads. Several original diagrammatic representations of the static modal analysis contribute to the overall understanding and implementation of the mathematical relations. This approach brings out new aspects of the interrelationship between the force and displacement methods, which strengthen their complementarity.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 6","pages":"3409 - 3440"},"PeriodicalIF":9.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141548689","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 Genetic Fuzzy Systems","authors":"Mohammad Jahani Moghaddam","doi":"10.1007/s11831-024-10157-9","DOIUrl":"https://doi.org/10.1007/s11831-024-10157-9","url":null,"abstract":"<p>Fuzzy Systems have shown their ability for solving a wide range of problems in different application domains. Genetic Algorithms are applied to provide the learning and adaptation capabilities for designing fuzzy systems, and this composition is called genetic fuzzy systems (GFSs). This paper reviews the field of GFSs consisting of the pioneer articles, the most cited papers, GFS milestones, recent research trends and, future outlooks. Additionally, there is paid attention to a short discussion on some critical considerations of recent developments and suggestions for potential future research directions.</p>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"1 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141525015","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":"Advancements and Prospects of Machine Learning in Medical Diagnostics: Unveiling the Future of Diagnostic Precision","authors":"Sohaib Asif, Yi Wenhui, Saif- ur-Rehman, Qurrat- ul-ain, Kamran Amjad, Yi Yueyang, Si Jinhai, Muhammad Awais","doi":"10.1007/s11831-024-10148-w","DOIUrl":"https://doi.org/10.1007/s11831-024-10148-w","url":null,"abstract":"<p>Machine learning (ML) has emerged as a versatile and powerful tool in various fields of medicine, revolutionizing early disease diagnosis, particularly in cases where traditional diagnostic approaches face challenges due to unclear or overlapping symptoms. This survey provides a comprehensive overview of the wide-ranging applications of ML techniques in detecting and diagnosing various diseases at an early stage, highlighting their potential to transform healthcare practices. The survey commences with a comprehensive review of commonly used ML algorithms, emphasizing their relevance and adaptability in medical domains. With a focus on disease diagnosis, we delve into the specific implementation of ML algorithms for early detection in prominent diseases, including cancer, COVID-19, diabetes, kidney diseases, and heart diseases. By analyzing the current state of research and developments, this survey provides valuable insights into how ML algorithms are being employed to enhance disease diagnosis accuracy and efficacy. In the domain of cancer diagnosis, ML techniques have made significant strides in analyzing medical imaging data, genomic profiling, and predictive modeling. These advancements have led to improved cancer detection rates, enabling timely interventions and personalized treatment plans. Additionally, the survey explores the pivotal role of ML in addressing the challenges posed by the COVID-19 pandemic. ML-based automated screening tools have demonstrated efficiency in detecting potential cases, while predictive modeling has been instrumental in estimating disease progression and optimizing resource allocation. Furthermore, ML’s contributions extend to chronic diseases such as diabetes, kidney diseases, and heart diseases, where it has shown promising results in predicting disease progression, enabling early intervention, and enhancing management strategies. In conclusion, this comprehensive survey showcases the transformative potential of ML in early disease diagnosis across various medical conditions. By providing valuable references and insights into future trends, it serves as a guiding resource for researchers and clinicians interested in leveraging ML technologies to improve patient care and make significant advancements in the field of medical diagnostics. With the capacity to decipher complex patterns and facilitate intelligent predictions, ML has emerged as a pivotal ally in the journey towards early disease detection and improved healthcare outcomes.</p>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"60 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141548691","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":"The State-of-the-Art on Time-Frequency Signal Processing Techniques for High-Resolution Representation of Nonlinear Systems in Engineering","authors":"Chunwei Zhang, Asma A. Mousavi, Sami F. Masri, Gholamreza Gholipour","doi":"10.1007/s11831-024-10153-z","DOIUrl":"https://doi.org/10.1007/s11831-024-10153-z","url":null,"abstract":"<p>One of the serious issues of traditional signal processing techniques in analyzing the responses of real-life structures is related to the presentation of fundamental information of nonlinear, non-stationary, and noisy signals with closely-spaced frequencies. To overcome this difficulty, numerous studies have been carried out recently to explore proper time-frequency signal processing techniques to efficiently present high-resolution representations for nonlinear characteristics of analyzed signals. Despite existing extensive reviews on vibration-based signal processing techniques in time and frequency domains for Structural Health Monitoring purposes, there exists no study in categorizing the signal processing techniques based on the feature extraction with time-frequency representations. To fill this gap, this paper presents a comprehensive state-of-the-art review on the applications of time-frequency signal processing techniques for damage detection, localization, and quantification in various structural systems. The progressive trend of time-frequency analysis methods is reviewed by summarizing their advantages and disadvantages, as well as recommendations of combination methods to be utilized for different applications in various complicated structural and mechanical systems.</p>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"3 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141525014","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: Structural Shape and Stress Control Techniques and their Applications","authors":"Ahmed Manguri, Najmadeen Saeed, Robert Jankowski","doi":"10.1007/s11831-024-10149-9","DOIUrl":"https://doi.org/10.1007/s11831-024-10149-9","url":null,"abstract":"<p>This review article presents prior studies on controlling shape and stress in flexible structures. The study offers a comprehensive survey of literature concerning the adjustment and regulation of shape, stress, or both in structures and emphasizes such control’s importance. The control of systems is classified into three primary classes: nodal movement control, axial force control, and controlling the two classes concurrently. Each class is thoroughly assessed, showcasing diverse methods anticipated by various scholars. Furthermore, the paper discusses methods to reduce the number of devices (actuators) to adjust and optimize actuators’ placement to achieve optimal structural control, considering the cost implications of numerous actuators. Additionally, various actuators are presented in detail, their advantages and disadvantages are also discussed. Moreover, the applications of the presented techniques are reviewed in detail, the essential recommendations for future work are also suggested.</p>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"44 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141525016","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 Deep Learning Techniques for Short-term Energy Load Forecasting Applications: A Review","authors":"Radhika Chandrasekaran, Senthil Kumar Paramasivan","doi":"10.1007/s11831-024-10155-x","DOIUrl":"https://doi.org/10.1007/s11831-024-10155-x","url":null,"abstract":"<p>Today, the majority of the leading power companies place a significant emphasis on forecasting the electricity load in the balance of power and administration. Meanwhile, since electricity is an integral component of every person’s contemporary life, energy load forecasting is necessary to afford the energy demand required. The expansion of the electrical infrastructure is a key factor in increasing sustainable economic growth, and the planning and control of the utility power system rely on accurate load forecasting. Due to uncertainty in energy utilization, forecasting is turning into a complex task, and it makes an impact on applications that include energy scheduling and management, price forecasting, etc. The statistical methods involving time series for regression analysis and machine learning techniques have been used in energy load forecasting extensively over the last few decades to precisely predict future energy demands. However, they have some drawbacks with limited model flexibility, generalization, and overfitting. Deep learning addresses the issues of handling unstructured and unlabeled data, automatic feature learning, non-linear model flexibility, the ability to handle high-dimensional data, and simultaneous computation using GPUs efficiently. This paper investigates factors influencing energy load forecasting, then discusses the most commonly used deep learning approaches in energy load forecasting, as well as evaluation metrics to evaluate the performance of the model, followed by bio-inspired algorithms to optimize the model, and other advanced technologies for energy load forecasting. This study discusses the research findings, challenges, and opportunities in energy load forecasting.</p>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141548690","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":"Comprehensive Analysis of Hydrodynamic Parameters for Fluidized Bed Gasifier to Enrich Renewable Hydrogen: A Review","authors":"Raj Kumar, Narayan Lal Panwar","doi":"10.1007/s11831-024-10150-2","DOIUrl":"https://doi.org/10.1007/s11831-024-10150-2","url":null,"abstract":"<p>The current global demand for renewable hydrogen is increasing due to the pressing need to address climate change and transition to sustainable energy sources. In this context, fluidized bed gasification is becoming more important as a versatile technology that shows promise for hydrogen production. Its high efficiency in converting solid fuels into syngas, a precursor to hydrogen, makes it a crucial player in the search for renewable energy solutions. This review aims to explain the crucial role of hydrodynamic parameters in optimizing fluidized bed gasification for enhanced hydrogen production. The objective is to thoroughly examine and synthesize existing research on hydrodynamic parameters in fluidized bed gasification, with a focus on their significant impact on renewable hydrogen production. By carefully analyzing the complex interactions of these variables, we aim to provide valuable insights that can guide the optimization of fluidized bed gasifiers toward increased hydrogen yields and improved quality.</p><h3 data-test=\"abstract-sub-heading\">Graphical Abstract</h3>\u0000","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"44 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141525017","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}