Frontiers in Artificial Intelligence最新文献

{"title":"Inpainting of damaged temple murals using edge- and line-guided diffusion patch GAN.","authors":"G Sumathi, M Uma Devi","doi":"10.3389/frai.2024.1453847","DOIUrl":"10.3389/frai.2024.1453847","url":null,"abstract":"<p><p>Mural paintings are vital cultural expressions, enriching our lives by beautifying spaces, conveying messages, telling stories, and evoking emotions. Ancient temple murals degrade over time due to natural aging, physical damage, etc. Preserving these cultural treasures is challenging. Image inpainting is often used for digital restoration, but existing methods typically overlook naturally degraded areas, using randomly generated binary masks or small, narrow regions for repair. This study proposes a novel architecture to reconstruct large areas of naturally degraded murals, maintaining intrinsic details, avoiding color bias, and preserving artistic excellence. The architecture integrates generative adversarial networks (GANs) and the diffusion model, including a whole structure formation network (WSFN), a semantic color network (SCN), and a diffusion mixture distribution (DIMD) discriminator. The WSFN uses the original image, a line drawing, and an edge map to capture mural details, which are then texturally inpainted in the SCN using gated convolution for enhanced results. Special attention is given to globally extending the receptive field for large-area inpainting. The model is evaluated using custom-degraded mural images collected from Tamil Nadu temples. Quantitative analysis showed superior results than state-of-the-art methods, with SSIM, MSE, PSNR, and LPIPS values of 0.8853, 0.0021, 29.8826, and 0.0426, respectively.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"7 ","pages":"1453847"},"PeriodicalIF":3.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11576470/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142682731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing smart city factories: enhancing industrial mechanical operations via deep learning techniques.","authors":"William Villegas-Ch, Jaime Govea, Walter Gaibor-Naranjo, Santiago Sanchez-Viteri","doi":"10.3389/frai.2024.1398126","DOIUrl":"10.3389/frai.2024.1398126","url":null,"abstract":"<p><p>In the contemporary realm of industry, the imperative for influential and steadfast systems to detect anomalies is critically recognized. Our study introduces a cutting-edge approach utilizing a deep learning model of the Long-Short Term Memory variety, meticulously crafted for real-time surveillance and mitigation of irregularities within industrial settings. Through the careful amalgamation of data acquisition and analytic processing informed by our model, we have forged a system adept at pinpointing anomalies with high precision, capable of autonomously proposing or implementing remedial measures. The findings demonstrate a marked enhancement in the efficacy of operations, with the model's accuracy surging to 95%, recall at 90%, and an F1 score reaching 92.5%. Moreover, the system has favorably impacted the environment, evidenced by a 25% decline in CO2 emissions and a 20% reduction in water usage. Our model surpasses preceding systems, showcasing significant gains in speed and precision. This research corroborates the capabilities of deep learning within the industrial sector. It underscores the role of automated systems in fostering more sustainable and efficient operations in the contemporary industrial landscape.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"7 ","pages":"1398126"},"PeriodicalIF":3.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11576463/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142682728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Catalyzing IVF outcome prediction: exploring advanced machine learning paradigms for enhanced success rate prognostication.","authors":"Seyed-Ali Sadegh-Zadeh, Sanaz Khanjani, Shima Javanmardi, Bita Bayat, Zahra Naderi, Amir M Hajiyavand","doi":"10.3389/frai.2024.1392611","DOIUrl":"10.3389/frai.2024.1392611","url":null,"abstract":"<p><p>This study addresses the research problem of enhancing <i>In-Vitro</i> Fertilization (IVF) success rate prediction by integrating advanced machine learning paradigms with gynecological expertise. The methodology involves the analysis of comprehensive datasets from 2017 to 2018 and 2010-2016. Machine learning models, including Logistic Regression, Gaussian NB, SVM, MLP, KNN, and ensemble models like Random Forest, AdaBoost, Logit Boost, RUS Boost, and RSM, were employed. Key findings reveal the significance of patient demographics, infertility factors, and treatment protocols in IVF success prediction. Notably, ensemble learning methods demonstrated high accuracy, with Logit Boost achieving an accuracy of 96.35%. The implications of this research span clinical decision support, patient counseling, and data preprocessing techniques, highlighting the potential for personalized IVF treatments and continuous monitoring. The study underscores the importance of collaboration between gynecologists and data scientists to optimize IVF outcomes. Prospective studies and external validation are suggested as future directions, promising to further revolutionize fertility treatments and offer hope to couples facing infertility challenges.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"7 ","pages":"1392611"},"PeriodicalIF":3.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11573753/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142677232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting patient reported outcome measures: a scoping review for the artificial intelligence-guided patient preference predictor.","authors":"Jeremy A Balch, A Hayes Chatham, Philip K W Hong, Lauren Manganiello, Naveen Baskaran, Azra Bihorac, Benjamin Shickel, Ray E Moseley, Tyler J Loftus","doi":"10.3389/frai.2024.1477447","DOIUrl":"10.3389/frai.2024.1477447","url":null,"abstract":"<p><strong>Background: </strong>The algorithmic patient preference predictor (PPP) has been proposed to aid in decision making for incapacitated patients in the absence of advanced directives. Ethical and legal challenges aside, multiple practical barriers exist for building a personalized PPP. Here, we examine previous work using machine learning to predict patient reported outcome measures (PROMs) for capacitated patients undergoing diverse procedures, therapies, and life events. Demonstrating robust performance in predicting PROMs for capacitated patients could suggest opportunities for developing a model tailored to incapacitated ones.</p><p><strong>Methods: </strong>We performed a scoping review of PubMed, Embase, and Scopus using the PRISMA-ScR guidelines to capture studies using machine learning to predict PROMs following a medical event alongside qualitative studies exploring a theoretical PPP.</p><p><strong>Results: </strong>Sixty-eight studies used machine learning to evaluate PROMs; an additional 20 studies focused on a theoretical PPP. For PROMs, orthopedic surgeries (<i>n</i> = 33) and spinal surgeries (<i>n</i> = 12) were the most common medical event. Studies used demographic (<i>n</i> = 30), pre-event PROMs (<i>n</i> = 52), comorbidities (<i>n</i> = 29), social determinants of health (<i>n</i> = 30), and intraoperative variables (<i>n</i> = 124) as predictors. Thirty-four different PROMs were used as the target outcome. Evaluation metrics varied by task, but performance was overall poor to moderate for the best reported scores. In models that used feature importance, pre-event PROMs were the most predictive of post-event PROMs. Fairness assessments were rare (<i>n</i> = 6). These findings reinforce the necessity of the integrating patient values and preferences, beyond demographic factors, to improve the development of personalized PPP models for incapacitated patients.</p><p><strong>Conclusion: </strong>The primary objective of a PPP is to estimate patient-reported quality of life following an intervention. Use of machine learning to predict PROMs for <i>capacitated</i> patients introduces challenges and opportunities for building a personalized PPP for <i>incapacitated</i> patients without advanced directives.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"7 ","pages":"1477447"},"PeriodicalIF":3.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11573790/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142677233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A generative AI-driven interactive listening assessment task.","authors":"Andrew Runge, Yigal Attali, Geoffrey T LaFlair, Yena Park, Jacqueline Church","doi":"10.3389/frai.2024.1474019","DOIUrl":"10.3389/frai.2024.1474019","url":null,"abstract":"<p><strong>Introduction: </strong>Assessments of interactional competence have traditionally been limited in large-scale language assessments. The listening portion suffers from construct underrepresentation, whereas the speaking portion suffers from limited task formats such as in-person interviews or role plays. Human-delivered tasks are challenging to administer at large scales, while automated assessments are typically very narrow in their assessment of the construct because they have carried over the limitations of traditional paper-based tasks to digital formats. However, computer-based assessments do allow for more interactive, automatically administered tasks, but come with increased complexity in task creation. Large language models present new opportunities for enhanced automated item generation (AIG) processes that can create complex content types and tasks at scale that support richer assessments.</p><p><strong>Methods: </strong>This paper describes the use of such methods to generate content at scale for an interactive listening measure of interactional competence for the Duolingo English Test (DET), a large-scale, high-stakes test of English proficiency. The Interactive Listening task assesses test takers' ability to participate in a full conversation, resulting in a more authentic assessment of interactive listening ability than prior automated assessments by positing comprehension and interaction as purposes of listening.</p><p><strong>Results and discussion: </strong>The results of a pilot of 713 tasks with hundreds of responses per task, along with the results of human review, demonstrate the feasibility of a human-in-the-loop, generative AI-driven approach for automatic creation of complex educational assessments at scale.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"7 ","pages":"1474019"},"PeriodicalIF":3.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571064/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142669235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing random forest predictive performance for foot and mouth disease outbreaks in Uganda: a calibrated uncertainty prediction approach for varying distributions.","authors":"Geofrey Kapalaga, Florence N Kivunike, Susan Kerfua, Daudi Jjingo, Savino Biryomumaisho, Justus Rutaisire, Paul Ssajjakambwe, Swidiq Mugerwa, Seguya Abbey, Mulindwa H Aaron, Yusuf Kiwala","doi":"10.3389/frai.2024.1455331","DOIUrl":"10.3389/frai.2024.1455331","url":null,"abstract":"<p><p>Foot-and-mouth disease poses a significant threat to both domestic and wild cloven-hoofed animals, leading to severe economic losses and jeopardizing food security. While machine learning models have become essential for predicting foot-and-mouth disease outbreaks, their effectiveness is often compromised by distribution shifts between training and target datasets, especially in non-stationary environments. Despite the critical impact of these shifts, their implications in foot-and-mouth disease outbreak prediction have been largely overlooked. This study introduces the Calibrated Uncertainty Prediction approach, designed to enhance the performance of Random Forest models in predicting foot-and-mouth disease outbreaks across varying distributions. The Calibrated Uncertainty Prediction approach effectively addresses distribution shifts by calibrating uncertain instances for pseudo-label annotation, allowing the active learner to generalize more effectively to the target domain. By utilizing a probabilistic calibration model, Calibrated Uncertainty Prediction pseudo-annotates the most informative instances, refining the active learner iteratively and minimizing the need for human annotation and outperforming existing methods known to mitigate distribution shifts. This reduces costs, saves time, and lessens the dependence on domain experts while achieving outstanding predictive performance. The results demonstrate that Calibrated Uncertainty Prediction significantly enhances predictive performance in non-stationary environments, achieving an accuracy of 98.5%, Area Under the Curve of 0.842, recall of 0.743, precision of 0.855, and an F1 score of 0.791. These findings underscore Calibrated Uncertainty Prediction's ability to overcome the vulnerabilities of existing ML models, offering a robust solution for foot-and-mouth disease outbreak prediction and contributing to the broader field of predictive modeling in infectious disease management.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"7 ","pages":"1455331"},"PeriodicalIF":3.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11564173/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142649200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using genAI in education: the case for critical thinking.","authors":"Chien Ching Lee, Malcolm Yoke Hean Low","doi":"10.3389/frai.2024.1452131","DOIUrl":"https://doi.org/10.3389/frai.2024.1452131","url":null,"abstract":"","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"7 ","pages":"1452131"},"PeriodicalIF":3.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11564148/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142649201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Political ideology shapes support for the use of AI in policy-making.","authors":"Tamar Gur, Boaz Hameiri, Yossi Maaravi","doi":"10.3389/frai.2024.1447171","DOIUrl":"10.3389/frai.2024.1447171","url":null,"abstract":"<p><p>In a world grappling with technological advancements, the concept of Artificial Intelligence (AI) in governance is becoming increasingly realistic. While some may find this possibility incredibly alluring, others may see it as dystopian. Society must account for these varied opinions when implementing new technologies or regulating and limiting them. This study (<i>N</i> = 703) explored Leftists' (liberals) and Rightists' (conservatives) support for using AI in governance decision-making amidst an unprecedented political crisis that washed through Israel shortly after the proclamation of the government's intentions to initiate reform. Results indicate that Leftists are more favorable toward AI in governance. While legitimacy is tied to support for using AI in governance among both, Rightists' acceptance is also tied to perceived norms, whereas Leftists' approval is linked to perceived utility, political efficacy, and warmth. Understanding these ideological differences is crucial, both theoretically and for practical policy formulation regarding AI's integration into governance.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"7 ","pages":"1447171"},"PeriodicalIF":3.0,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11557559/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142629583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time crop row detection using computer vision- application in agricultural robots.","authors":"Md Nazmuzzaman Khan, Adibuzzaman Rahi, Veera P Rajendran, Mohammad Al Hasan, Sohel Anwar","doi":"10.3389/frai.2024.1435686","DOIUrl":"10.3389/frai.2024.1435686","url":null,"abstract":"<p><p>The goal of achieving autonomous navigation for agricultural robots poses significant challenges, mostly arising from the substantial natural variations in crop row images as a result of weather conditions and the growth stages of crops. The processing of the detection algorithm also must be significantly low for real-time applications. In order to address the aforementioned requirements, we propose a crop row detection algorithm that has the following features: Firstly, a projective transformation is applied to transform the camera view and a color-based segmentation is employed to distinguish crop and weed from the background. Secondly, a clustering algorithm is used to differentiate between the crop and weed pixels. Lastly, a robust line-fitting approach is implemented to detect crop rows. The proposed algorithm is evaluated throughout a diverse range of scenarios, and its efficacy is assessed in comparison to four distinct existing solutions. The algorithm achieves an overall intersection over union (IOU) of 0.73 and exhibits robustness in challenging scenarios with high weed growth. The experiments conducted on real-time video featuring challenging scenarios show that our proposed algorithm exhibits a detection accuracy of over 90% and is a viable option for real-time implementation. With the high accuracy and low inference time, the proposed methodology offers a viable solution for autonomous navigation of agricultural robots in a crop field without damaging the crop and thus can serve as a foundation for future research.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"7 ","pages":"1435686"},"PeriodicalIF":3.0,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11558879/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142629588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A brief reference to AI-driven audible reality (AuRa) in open world: potential, applications, and evaluation.","authors":"Ömer Ates, Garima Pandey, Athanasios Gousiopoulos, Theodoros G Soldatos","doi":"10.3389/frai.2024.1424371","DOIUrl":"https://doi.org/10.3389/frai.2024.1424371","url":null,"abstract":"<p><p>Recent developments on artificial intelligence (AI) and machine learning (ML) techniques are expected to have significant impact on public health in several ways. Indeed, modern AI/ML methods have been applied on multiple occasions on topics ranging from drug discovery and disease diagnostics to personalized medicine, medical imaging, and healthcare operations. While such developments may improve several quality-of-life aspects (such as access to health services and education), it is important considering that some individuals may face more challenges, particularly in extreme or emergency situations. In this work, we focus on utilizing AI/ML components to support scenarios when visual impairment or other limitations hinder the ability to interpret the world in this way. Specifically, we discuss the potential and the feasibility of automatically transferring key visual information into audio communication, in different languages and in real-time-a setting which we name '<i>au</i>dible <i>r</i>e<i>a</i>lity' (AuRa). We provide a short guide to practical options currently available for implementing similar solutions and summarize key aspects for evaluating their scope. Finally, we discuss diverse settings and functionalities that AuRA applications could have in terms of broader impact, from a social and public health context, and invite the community to further such digital solutions and perspectives soon.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"7 ","pages":"1424371"},"PeriodicalIF":3.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11543578/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142629550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}