Archives of Computational Methods in Engineering最新文献

{"title":"A Critical Review on Segmentation of Glioma Brain Tumor and Prediction of Overall Survival","authors":"Novsheena Rasool,&nbsp;Javaid Iqbal Bhat","doi":"10.1007/s11831-024-10188-2","DOIUrl":"10.1007/s11831-024-10188-2","url":null,"abstract":"<div><p>In recent years, the surge in glioma brain tumor cases has positioned it as the 10th most prevalent tumor affecting individuals across diverse age groups. Gliomas, characterized by their invasive nature, unpredictable localization, and heterogeneous subregions, pose a substantial threat to public health. Accurate segmentation of glioma subregions within magnetic resonance imaging (MRI) images is pivotal for the efficient planning of treatment and the prognostication of overall patient survival. This review examines recent advancements in glioma brain tumor segmentation (BTS) and overall survival (OS) prediction while addressing inherent biases and proposing innovative solutions. We explore the evolution of convolutional neural network (CNN) architectures, from traditional 2D CNNs to advanced 2.5D and 3D CNNs, which have greatly enhanced segmentation accuracy and efficiency. Furthermore, cutting-edge techniques for BTS, including attention mechanisms, ensemble methods, transformer-based models, and generative adversarial networks (GANs), are discussed. Additionally, we examine machine learning (ML) models for OS prediction, including support vector machines (SVM) and random forest regressors (RFRs), as well as pioneering methods such as radiomics-based approaches, consensus-based classifiers, and explainable artificial intelligence (XAI). By comparing different preprocessing techniques, model architectures, data sources, and evaluation metrics, we identify the most effective methods and emphasize the importance of collaboration in developing reliable prognostic tools. By consolidating current research, this paper advances ongoing investigations and offers a visionary path for future studies, providing guidance to healthcare stakeholders for refining patient care strategies in glioma management.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 3","pages":"1525 - 1569"},"PeriodicalIF":9.7,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comprehensive Review on Heart Disease Risk Prediction using Machine Learning and Deep Learning Algorithms","authors":"Vishnu Vardhana Reddy Karna,&nbsp;Viswavardhan Reddy Karna,&nbsp;Varaprasad Janamala,&nbsp;V. N. Koteswara Rao Devana,&nbsp;V. Ravi Sankar Ch,&nbsp;Aravinda Babu Tummala","doi":"10.1007/s11831-024-10194-4","DOIUrl":"10.1007/s11831-024-10194-4","url":null,"abstract":"<div><p>Cardiovascular diseases claim approximately 17.9 million lives annually, with heart attacks and strokes accounting for over 80% of these deaths. Key risk factors, including hypertension, hyperglycemia, dyslipidemia, and obesity, are identifiable, offering opportunities for timely intervention and reduced mortality. Early detection of heart disease enables individuals to adopt lifestyle changes or seek medical treatment. However, conventional diagnostic methods, such as electrocardiograms—commonly used in clinics and hospitals to detect abnormal heart rhythms—are not effective in identifying actual heart attacks. Additionally, angiography, while more precise, is an invasive method, financial strain on patients, and high chances of incorrect diagnosis, highlighting the need for alternative approaches. The main goal of this study was to assess the accuracy of machine learning techniques, including both individual and combined classifiers, in early detection of heart diseases. Furthermore, the study aims to highlight areas where additional research is necessary. Our investigation covers a decade period from 2014 to 2024, including a thorough review of pertinent literature from international conferences and top journals from the databases like Springer, ScienceDirect, IEEEXplore, Web of Science, PubMed, MDPI, Hindawi and so on. The following keywords were used to search the articles: heart disease risk, heart disease prediction, data mining, data preprocessing, machine learning algorithms, ensemble classifiers, deep learning algorithms, feature selection, hyperparameter optimization techniques. We examine the methodologies used and evaluate their effectiveness in predicting cardiovascular conditions. Our findings reveal notable progress in applying machine learning and deep learning in cardiology. The study concludes by proposing a framework that incorporates current machine learning techniques to enhance heart disease prediction.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 3","pages":"1763 - 1795"},"PeriodicalIF":9.7,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Survey on Automatic Image Captioning Approaches: Contemporary Trends and Future Perspectives","authors":"Garima Salgotra,&nbsp;Pawanesh Abrol,&nbsp;Arvind Selwal","doi":"10.1007/s11831-024-10190-8","DOIUrl":"10.1007/s11831-024-10190-8","url":null,"abstract":"<div><p>The automatic generation of image captions is one of the complex computer vision tasks that involve integration of object detection and natural language processing (NLP). In recent times, one of the significant aspects is to design image captioning approaches that accurately and efficiently generate appropriate image captions in a particular domain. With the emergence of deep learning paradigms, the task of image captioning becomes comparatively easier than traditional template-based approaches. In this article, we expound an in-depth examination of state of the art (SOTA) image captioning methods, along with the key conceptions. Besides, a comparative analysis of evaluation protocols is presented that are presently used to access the efficacy of the algorithms. Moreover, the study reveals open research issues in the existing methods that can be further investigated by the research community. One of the key challenges is to develop larger corpora of language specific dataset to design image captioning approaches in other regional languages such as Hindi, Marathi, Sanskrit, Telugu, and Gujarati etc. Furthermore, designing accurate and efficient image captioning approaches requisite the notion of attention mechanism in the images.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 3","pages":"1459 - 1497"},"PeriodicalIF":9.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection and Identification of Hazardous Hidden Objects in Images: A Comprehensive Review","authors":"Satyajit Swain,&nbsp;K. Suganya Devi","doi":"10.1007/s11831-024-10173-9","DOIUrl":"10.1007/s11831-024-10173-9","url":null,"abstract":"<div><p>Hidden object detection has attracted a lot of attention recently due to its importance in security surveillance and other real-world applications. It is considered one of the most challenging tasks in computer vision. Thanks to deep learning for playing a significant role in the rapid technical evolution in this field over the past decade. This article presents a roadmap of hidden object detection, starting from its insightful evolution in 1984, and extensively reviews the technical evolution and shifts in detection approaches. To the best of our knowledge, this is the first ever review work carried out in this field. Various aspects related to hidden object detection have been discussed, including basic building blocks of the detection system, historical milestone detectors, detection datasets, challenges, pre-processing techniques, modern state-of-the-art detection frameworks, and the various evaluation metrics used to assess the detection performance. Towards the end, the paper emphasizes on some unanswered research concerns and possible future prospects in the field of hidden object detection. This review paper aims to serve as a valuable resource for researchers, practitioners, and enthusiasts seeking a thorough understanding of the concepts, advancements, and challenges in this dynamic area of computer vision as hidden object detection continues to have an impact on a variety of interdisciplinary fields of research.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 2","pages":"1135 - 1183"},"PeriodicalIF":9.7,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative Adversarial Networks (GANs) for Medical Image Processing: Recent Advancements","authors":"Mohd Ali,&nbsp;Mehboob Ali,&nbsp;Mubashir Hussain,&nbsp;Deepika Koundal","doi":"10.1007/s11831-024-10174-8","DOIUrl":"10.1007/s11831-024-10174-8","url":null,"abstract":"<div><p>Generative Adversarial Networks (GANs) constitute an advanced category of deep learning models that have significantly transformed the domain of generative modelling. They demonstrate a profound capability to produce realistic and high-quality synthetic data across various domains. Recently, GANs have also emerged as a powerful and innovative approach in medical image processing. Numerous scholarly investigations consistently highlight the superiority of GAN-based methodologies in this context. The generation of realistic synthetic images aims to advance segmentation precision, augment image quality, and facilitate multimodal analysis. These enhancements significantly bolster the analytical capabilities of medical professionals, leading to more precise diagnostic evaluations and the formulation of personalized treatment plans, thereby contributing to improved patient prognosis. In this work, we rigorously review the latest advancements in the application of Generative Adversarial Networks (GANs) within the domain of medical imaging, encompassing research published between 2018 and 2024. The corpus of literature selected for this review is derived from the most relevant and authoritative databases, including Elsevier, Springer, IEEE Xplore, and Google Scholar, among others. This review rigorously evaluates scholarly publications employing Generative Adversarial Networks (GANs) for the synthesis and generation of medical images, segmentation of medical imaging data, image-to-image translation in medical contexts, and denoising or reconstruction of medical imagery. The findings of this review present a thorough synthesis of contemporary applications of Generative Adversarial Networks (GANs) in the domain of medical imaging. This investigation serves as a prospective reference in the realm of GAN utilization for medical image processing, offering guidance and insights for current and future research endeavors.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 2","pages":"1185 - 1198"},"PeriodicalIF":9.7,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning Approaches for Early Prediction of Conversion from MCI to AD using MRI and Clinical Data: A Systematic Review","authors":"Gelareh Valizadeh,&nbsp;Reza Elahi,&nbsp;Zahra Hasankhani,&nbsp;Hamidreza Saligheh Rad,&nbsp;Ahmad Shalbaf","doi":"10.1007/s11831-024-10176-6","DOIUrl":"10.1007/s11831-024-10176-6","url":null,"abstract":"<div><p>Due to the absence of definitive treatment for Alzheimer’s disease (AD), slowing its development is essential. Accurately predicting the conversion of mild cognitive impairment (MCI) -a potential early stage of AD- to AD is challenging due to the subtle distinctions between individuals who will develop AD and those who will not. As an increasing body of evidence in the literature suggests, advanced magnetic resonance imaging (MRI) scans, coupled with high-performance computing techniques and novel deep learning techniques, have revolutionized the ability to predict MCI to AD conversion. This study systematically reviewed the publications from 2013 to 2023 (July) to investigate the contribution of deep learning in predicting the MCI conversion to AD, concentrating on the MRI data (structural or functional) and clinical information. The search conducted across seven different databases yielded a total of 2273 studies. Out of these, 78 relevant studies were included, which were thoroughly reviewed, and their essential details and findings were extracted. Furthermore, this study comprehensively explores the challenges associated with predicting the conversion from MCI to AD using deep learning methods with MRI data. Also, it identifies potential solutions to address these challenges. The research field of predicting MCI to AD conversion from MRI data using deep learning techniques is constantly evolving. There is an increasing focus on employing explainable approaches to improve transparency in the analysis process. The paper concludes with an overview of future perspectives and recommends conducting further studies in MCI to AD conversion prediction using deep learning methods.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 2","pages":"1229 - 1298"},"PeriodicalIF":9.7,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Survey of Artificial Intelligence Applications in Wind Energy Forecasting","authors":"Poonam Dhaka,&nbsp;Mini Sreejeth,&nbsp;M. M. Tripathi","doi":"10.1007/s11831-024-10182-8","DOIUrl":"10.1007/s11831-024-10182-8","url":null,"abstract":"<div><p>Renewable energy forecasting, such as Wind and Solar forecasting, is becoming more critical as the demand for clean energy increases. Thus, it is crucial to enhance the accuracy of wind power predictions to ensure electrical energy system’s efficient, reliable, and safe operation. Research on wind forecasting has increased dramatically over the past 10 years due to the success of Artificial Intelligence (AI) technologies like machine learning and deep learning. Despite their potential, AI approaches are fraught with uncertainties. It remains unclear how certain factors may influence the accuracy of AI algorithm predictions. This study reviews AI applications in Wind energy forecasting, aiming to provide an analysis of (1) AI-based structures and optimizers for Wind forecasting, (2) forecast performance evaluation for Deterministic and Probabilistic techniques.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 8","pages":"4853 - 4878"},"PeriodicalIF":9.7,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142265953","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":"Biomechanical Properties of the Large Intestine","authors":"Minghui Wang,&nbsp;Ji Liu,&nbsp;Taiyu Han,&nbsp;Wei Zhou,&nbsp;Yuhui Zhou,&nbsp;Hongliu Yu","doi":"10.1007/s11831-024-10177-5","DOIUrl":"10.1007/s11831-024-10177-5","url":null,"abstract":"<div><p>The large intestine is an important part of the human digestive system and the target of many diseases. The biomechanical properties of the large intestine are closely related to the proper functioning of its mechanical behavior. Therefore, the study of its biomechanical properties can help to better understand the key factors of lesions and provide a theoretical basis for the research and application of disease treatment, artificial anal sphincter, and other related medical devices. Physiologic structure of the large intestine is the basis for the study of its biomechanical properties. Constitutive models are commonly used to describe the biomechanical properties of soft tissues and to provide a mathematical characterization of the effects of biological components on the behavior of materials. The studies and results of biomechanical experiments provide a basis for determining the material parameters and the causes of functional damage to the material. In this paper, the biomechanical properties of the large intestine are investigated from two aspects: active properties and passive properties, and three perspectives: physiological structure, constitutive models, and biomechanical experiments. The paper concludes that their effective combination is an important comprehensive method to study the biomechanical properties of the large intestine. It provides a new perspective for the study of biomechanical properties of the large intestine, and provides a theoretical basis for future related biomechanical studies and the development of related medical devices.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 2","pages":"645 - 661"},"PeriodicalIF":9.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142205799","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":"Multi-objective Ant Colony Optimization: Review","authors":"Mohammed A. Awadallah,&nbsp;Sharif Naser Makhadmeh,&nbsp;Mohammed Azmi Al-Betar,&nbsp;Lamees Mohammad Dalbah,&nbsp;Aneesa Al-Redhaei,&nbsp;Shaimaa Kouka,&nbsp;Oussama S. Enshassi","doi":"10.1007/s11831-024-10178-4","DOIUrl":"10.1007/s11831-024-10178-4","url":null,"abstract":"<div><p>Ant colony optimization (ACO) algorithm is one of the most popular swarm-based algorithms inspired by the behavior of an ant colony to find the shortest path for food. The multi-objective ACO (MOACO) is a modified variant of ACO introduced to deal with multi-objective optimization problems (MOPs). The MOACO is seeking to find a set of solutions that achieve trade-offs between the different objectives, which help the decision-makers select the most appreciated solution according to their preferences. Recently, a large number of MOACO research works have been published in the literature, reaching 384 research papers according to the SCOPUS database. In this review paper, 189 different research works of MOACOs published in only scientific journals are considered. Through this research, researchers will gain insights into the expansion of MOACO, the theoretical foundations of MOPs and the MOACO algorithm, various MOACO variants documented in existing literature will be reviewed, and the specific application domains where MOACO has been implemented will be summarized. The critical discussion of the MOACO advantages and limitations is analyzed to provide better insight into the main research gaps in this domain. Finally, the conclusion and some possible future research directions of MOACO are also given in this work.\u0000</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 2","pages":"995 - 1037"},"PeriodicalIF":9.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142205798","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":"Quantum Computational Intelligence Techniques: A Scientometric Mapping","authors":"Mini Arora,&nbsp;Kapil Gupta","doi":"10.1007/s11831-024-10183-7","DOIUrl":"10.1007/s11831-024-10183-7","url":null,"abstract":"<div><p>Computational intelligence has previously demonstrated its existence beyond the limitations of binary variables and Turing Machines. Using quantum concepts, Deutsch (1985) and Grover (1996) provide massive parallelism and searching techniques, vastly expanding the computational capacity of soft computing. This paper aims to analyze articles that consider both computational intelligence and quantum computing, referred to here as the quantum computational intelligence (QCI) category, to solve non-deterministic problems efficiently. The category includes 3067 research papers published from 2014 to 2023 that are indexed in high-quality databases like SCI and SCOPUS. This study examines QCI publishing patterns utilizing scientometric analysis employing co-occurrence, co-citation, and bibliographic coupling methodologies. Additionally, it provides insights into the citation patterns of publications, affiliations, and authors. China, USA, and India published more than half (53%) of the articles. The primary emphasis of application fields throughout this decade includes ‘Ground State Preparation’ and ‘Financial Forecasting’ among others. The pertinent keywords that have lately been studied are quantum particle swarm optimization (2022), optimization (2021), quantum circuits (2020), and deep learning (2019). Five quantum-based computation techniques were identified using a mix of critical review and cluster analysis: quantum machine learning, quantum neural networks, quantum particle swarm optimization, quantum variational Monte Carlo, and quantum-inspired evolutionary algorithms. The primary objective of this study is to address key queries that could contribute to future research in this field.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 3","pages":"1399 - 1425"},"PeriodicalIF":9.7,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142205802","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}