Archives of Computational Methods in Engineering最新文献

{"title":"Learning Models in Crowd Analysis: A Review","authors":"Silky Goel, Deepika Koundal, Rahul Nijhawan","doi":"10.1007/s11831-024-10151-1","DOIUrl":"https://doi.org/10.1007/s11831-024-10151-1","url":null,"abstract":"<p>Crowd detection and counting are important tasks in several applications of crowd analysis including traffic management, public safety and event planning. Automatic crowd counting using images and videos is an intriguing but complex issue that has generated considerable interest in computer vision. During the past several years, various learning models have been developed by considering several factors such as model design, input pathways, learning paradigms, computing complexity and accuracy that increases cutting-edge performance. In this work, the most critical advances in the crowd analysis field are reviewed methodically and thoroughly. Numerous crowd counting models have been arranged according to how well these models perform on different datasets using various learning approaches and evaluation metrics like mean average error and mean square error. This work provides insight into the effectiveness of different learning models for crowd analysis. It will be helpful for researchers and practitioners in choosing the appropriate model for their specific applications.</p>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"58 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141525019","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 of Machine Learning Algorithms and Its Application in Groundwater Quality Prediction","authors":"Harsh Pandya,&nbsp;Khushi Jaiswal,&nbsp;Manan Shah","doi":"10.1007/s11831-024-10126-2","DOIUrl":"10.1007/s11831-024-10126-2","url":null,"abstract":"&lt;div&gt;&lt;p&gt;Groundwater is among the utmost essential renewable resources for every organism existing on Earth. Assessing water quality is critical for the ecosystem’s stability and conservation. The overall water quality possesses a significant effect on human being wellness and environmental preservation. Numerous applications of water exist, including those related to industries, agriculture, and consumption. The water quality index (WQI) is an essential metric for assessing water management effectiveness. By its biological, physical, and physiological features, water quality assesses whether water is suitable for a specific application or not. Water quality analysis has become a big concern in today’s world because of industrialization, industry, farming techniques, and people’s behavior. Quality of water has traditionally been examined using expensive testing facilities and numerical procedures, enabling monitoring in real-time obsolete. Improper quality of groundwater necessitates an additional feasible and affordable remedy. The algorithmic learning-based categorization technique looks to be promising for quick identification and estimation of water quality. Predicting the quality of water has been done effectively using machine learning algorithms. The technological investigation of computer algorithms as well as mathematical models that networks of computers employ to complete a certain task without having to be explicitly programmed is referred to as machine learning (ML). The major benefit associated with algorithmic machine learning models is that as an algorithm knows how to utilize data, it can perform its function independently. This work comprehensively examines three major machine learning techniques: Decision Tree, Regression Model, and Support Vector Machine. Features including total coliform, electric conductivity, biological oxygen demand, pH, dissolved oxygen, and nitrate determine the water quality. In this paper, many prior research that employed machine learning techniques for determining water quality in diverse regions were examined. A comparison of past research involving these algorithms, assessment methodologies, and acquired outcomes is offered. We performed a thorough analysis of the cutting-edge ML algorithms used to predict groundwater quality. As part of our methodology, we analysed a wide range of research, looked into the use of conventional and cutting-edge ML techniques, pre-processing techniques, feature selection techniques, and data augmentation methods. The findings of this study will help with groundwater development planning and will enhance the Machine learning applications in improving the quality of groundwater. Our analysis demonstrates the adaptability of ML techniques in predicting groundwater quality. We discovered that ML models, such as deep learning, ensemble approaches, neural networks, support vector machines, and linear regression, have been successfully used to predict the quality of groundwa","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 8","pages":"4633 - 4654"},"PeriodicalIF":9.7,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141525018","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":"Various Methods for Computing Risk Factors of Down Syndrome in Fetus","authors":"Sushil Kumar, K. Selvakumar","doi":"10.1007/s11831-024-10158-8","DOIUrl":"https://doi.org/10.1007/s11831-024-10158-8","url":null,"abstract":"<p>There is a chromosomal defect that significantly affects an individual’s life is Down syndrome. Early identification of Down syndrome is crucial for an accurate assessment of the fetus. The process of assess the fetus includes measurement of the crown rump length, fetal heart rate, short arm or thighs bones length, nasal bone present or absent and the thickness of fluid behind neck. And the process are done during first and second trimester of pregnancy. Various invasive and noninvasive screenings are used for Down syndrome diagnosis. Research on diagnosing Down syndrome has been extensively documented. Additionally, this survey includes various techniques using deep learning for detecting the availability of Down Syndrome and does analysis of image processing methods and formulas for its diagnosis.</p>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"23 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141548692","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 of Probabilistic Approaches for Assessing the Liquefaction Hazard in Urban Areas","authors":"Alejandro Cruz,&nbsp;Shaghayegh Karimzadeh,&nbsp;Nicola Chieffo,&nbsp;Eimar Sandoval,&nbsp;Paulo B. Lourenço","doi":"10.1007/s11831-024-10124-4","DOIUrl":"10.1007/s11831-024-10124-4","url":null,"abstract":"<div><p>Several probabilistic liquefaction triggering approaches, or liquefaction manifestation severity approaches, have been developed to consider the uncertainties related to liquefaction and its manifestations. Probabilistic approaches are essential for vulnerability and risk models that considers the consequences of liquefaction on building performance. They may be incorporated into a performance-based earthquake engineering framework through a fully probabilistic liquefaction hazard assessment. The objective is to effectively incorporate spatial interaction of two concurrent hazards, specifically earthquake-induced shaking, and liquefaction, and to develop a robust multi-hazard framework applicable to regions with limited input data. For this purpose, it is necessary to establish, according to the available probabilistic liquefaction triggering or manifestation severity assessment approaches, which set of approaches aligns optimally with vulnerability and risk models. Thus, this paper discusses the current methodologies on the ongoing probabilistic liquefaction hazard assessment approaches with the aim of defining a reliable model specific for areas with a non-liquefiable surface layer over a liquefiable layer.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 8","pages":"4673 - 4708"},"PeriodicalIF":9.7,"publicationDate":"2024-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11831-024-10124-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552681","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":"The Effect of Loading Inclination and Eccentricity on the Bearing Capacity of Shallow Foundations: A Review","authors":"Lysandros Pantelidis,&nbsp;Abdelaziz Meddah","doi":"10.1007/s11831-024-10113-7","DOIUrl":"10.1007/s11831-024-10113-7","url":null,"abstract":"<div><p>This paper provides a comprehensive review on the effect of load inclination and eccentricity on the bearing capacity of shallow foundations. Regarding load eccentricity, Meyerhof’s intuitive formula <span>({B}{prime}=B-2{e}_{b})</span> aligns well with finite element analyses, though it is slightly conservative. Analysis using finite element results revealed the more accurate formula <span>(B-1.9{e}_{b})</span>. Concerning load inclination factors, numerous such factors exist in the literature. However, most are either intuitive or derived from small-scale experimental results, rendering them unreliable due to the significant impact of model scale on the bearing capacity of footings. Based on numerical results, it is proposed that all inclination factors (namely <span>({i}_{c})</span>, <span>({i}_{gamma })</span> and <span>({i}_{q})</span>) can be reliably expressed by a formula of the form <span>({left(1-{f}_{1}cdot {tan }left({f}_{3}delta right)right)}^{{f}_{2}})</span>, where <span>(delta)</span> is the inclination angle of the loading with respect to the vertical, <span>({f}_{1})</span> and <span>({f}_{3})</span> are coefficients and <span>({f}_{2}=3)</span>. The latter ensures smooth transition from the bearing capacity failure to the sliding failure as <span>(delta)</span> increases. It is also observed that many <span>(i-)</span> factors in the literature and various design standards employ an impermissible combination of sliding resistance at the footing-soil interface and Mohr–Coulomb bearing capacity failure under the footing. Moreover, it is shown that only the <span>({i}_{c})</span> factor depends on the angle of internal friction of soil. Finally, Vesic’s 1975 “<i>m</i>” interpolation formula largely falls short in accurately representing the effect of the direction of the horizontal loading.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 7","pages":"4189 - 4208"},"PeriodicalIF":9.7,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11831-024-10113-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141548693","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":"Demystifying ChatGPT: An In-depth Survey of OpenAI’s Robust Large Language Models","authors":"Pronaya Bhattacharya,&nbsp;Vivek Kumar Prasad,&nbsp;Ashwin Verma,&nbsp;Deepak Gupta,&nbsp;Assadaporn Sapsomboon,&nbsp;Wattana Viriyasitavat,&nbsp;Gaurav Dhiman","doi":"10.1007/s11831-024-10115-5","DOIUrl":"10.1007/s11831-024-10115-5","url":null,"abstract":"&lt;div&gt;&lt;p&gt;Recent advancements in natural language processing (NLP) have catalyzed the development of models capable of generating coherent and contextually relevant responses. Such models are applied across a diverse array of applications, including but not limited to chatbots, expert systems, question-and-answer robots, and language translation systems. Large Language Models (LLMs), exemplified by OpenAI’s Generative Pretrained Transformer (GPT), have significantly transformed the NLP landscape. They have introduced unparalleled abilities in generating text that is not only contextually appropriate but also semantically rich. This evolution underscores a pivotal shift towards more sophisticated and intuitive language understanding and generation capabilities within the field. Models based on GPT are developed through extensive training on vast datasets, enabling them to grasp patterns akin to human writing styles and deliver insightful responses to intricate questions. These models excel in condensing text, extending incomplete passages, crafting imaginative narratives, and emulating conversational exchanges. However, GPT LLMs are not without their challenges, including ethical dilemmas and the propensity for disseminating misinformation. Additionally, the deployment of these models at a practical scale necessitates a substantial investment in training and computational resources, leading to concerns regarding their sustainability. ChatGPT, a variant rooted in transformer-based architectures, leverages a self-attention mechanism for data sequences and a reinforcement learning-based human feedback (RLHF) system. This enables the model to grasp long-range dependencies, facilitating the generation of contextually appropriate outputs. Despite ChatGPT marking a significant leap forward in NLP technology, there remains a lack of comprehensive discourse on its architecture, efficacy, and inherent constraints. Therefore, this survey aims to elucidate the ChatGPT model, offering an in-depth exploration of its foundational structure and operational efficacy. We meticulously examine Chat-GPT’s architecture and training methodology, alongside a critical analysis of its capabilities in language generation. Our investigation reveals ChatGPT’s remarkable aptitude for producing text indistinguishable from human writing, whilst also acknowledging its limitations and susceptibilities to bias. This analysis is intended to provide a clearer understanding of ChatGPT, fostering a nuanced appreciation of its contributions and challenges within the broader NLP field. We also explore the ethical and societal implications of this technology, and discuss the future of NLP and AI. Our study provides valuable insights into the inner workings of ChatGPT, and helps to shed light on the potential of LLMs for shaping the future of technology and society. The approach used as Eco-GPT, with a three-level cascade (GPT-J, J1-G, GPT-4), achieves 73% and 60% cost savings in CaseHold an","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 8","pages":"4557 - 4600"},"PeriodicalIF":9.7,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141525020","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 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/s11831-024-10143-1","DOIUrl":"https://doi.org/10.1007/s11831-024-10143-1","url":null,"abstract":"","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"39 22","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141339827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational ElectroHydroDynamics in microsystems: A Review of Challenges and Applications","authors":"C. Narváez-Muñoz, Ali Reza Hashemi, Mohammad R. Hashemi, Luis Javier Segura, P. Ryzhakov","doi":"10.1007/s11831-024-10147-x","DOIUrl":"https://doi.org/10.1007/s11831-024-10147-x","url":null,"abstract":"","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"47 38","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141339502","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":"AI Techniques in Detection of NTLs: A Comprehensive Review","authors":"Rakhi Yadav,&nbsp;Mainejar Yadav,&nbsp; Ranvijay,&nbsp;Yashwant Sawle,&nbsp;Wattana Viriyasitavat,&nbsp;Achyut Shankar","doi":"10.1007/s11831-024-10137-z","DOIUrl":"10.1007/s11831-024-10137-z","url":null,"abstract":"<div><p>In the operation of power grid, worldwide, non-technical losses (NTLs) occur in a massive amount of proportion which is observed up to 40% of the total electric transmission and distribution losses. These dominant losses severely affect to adverse the performance of all the private and public distribution sectors. By rectifying these NTLs, the necessity of establishing new power plants will automatically be cut down. Hence, NTLs have become a critical challenge to do research in this emerging area for researchers of power systems due to the limitations of the current methodologies to detect and fix up these prominent type of losses. The existing survey so for basically contains the detail of identification of non-technical losses by machine and deep learning methods while this paper is a complete trouble shooting to resolve this issue by systematic approach. To address this, causes of NTLs along with its impact on economies and types of NTLs are elaborated in various countries. In addition, we have also prepared a comparative analysis based on several essential parameters. Further, implementation process of detection of NTLs or electricity theft based on Machine Learning or Deep Learning has also been demonstrated. Moreover, major challenges of detection of NTLs or electricity theft based on ML and Deep Learning, and its possible solutions are also described. Hence, definitely this comprehensive survey will help to the leading researchers to reach a new height in this thrust area.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 8","pages":"4879 - 4892"},"PeriodicalIF":9.7,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141345073","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-Based Assessment of the Influence of Nanoparticles on Biodiesel Engine Performance and Emissions: A critical review","authors":"Chetan Pawar, B. Shreeprakash, Beekanahalli Mokshanatha, K. Nikam, Nitin Motgi, L. D. Jathar, Sagar D. Shelare, Shubham Sharma, S. Dwivedi, P. S. Bains, Abhinav Kumar, Mohamed Abbas","doi":"10.1007/s11831-024-10144-0","DOIUrl":"https://doi.org/10.1007/s11831-024-10144-0","url":null,"abstract":"","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"63 9","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141347055","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}