信息工程最新文献

{"title":"AI-Based Advanced Approaches and Dry Eye Disease Detection Based on Multi-Source Evidence: Cases, Applications, Issues, and Future Directions","authors":"M. Wang, Lumin Xing, Yi Pan, Feng Gu, Junbin Fang, Xiangrong Yu, C. Pang, Kelvin Kam-Lung Chong, Carol Yim-Lui Cheung, Xulin Liao, Xiaoxiao Fang, Jie Yang, Ruoyu Zhou, Xiaoshu Zhou, Fengling Wang, Wenjian Liu","doi":"10.26599/bdma.2023.9020024","DOIUrl":"https://doi.org/10.26599/bdma.2023.9020024","url":null,"abstract":"","PeriodicalId":52355,"journal":{"name":"Big Data Mining and Analytics","volume":null,"pages":null},"PeriodicalIF":13.6,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141229812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Energy Consumption Using Stacked LSTM Snapshot Ensemble","authors":"Mona Ahamd Alghamdi, Abdullah S. Al-Malaise Al-Ghamdi, Mahmoud Ragab","doi":"10.26599/bdma.2023.9020030","DOIUrl":"https://doi.org/10.26599/bdma.2023.9020030","url":null,"abstract":"","PeriodicalId":52355,"journal":{"name":"Big Data Mining and Analytics","volume":null,"pages":null},"PeriodicalIF":13.6,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141232601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combining machine and deep transfer learning for mediastinal lymph node evaluation in patients with lung cancer","authors":"Hui XIE ,&nbsp;Jianfang ZHANG ,&nbsp;Lijuan DING ,&nbsp;Tao TAN ,&nbsp;Qing LI","doi":"10.1016/j.vrih.2023.08.002","DOIUrl":"https://doi.org/10.1016/j.vrih.2023.08.002","url":null,"abstract":"<div><h3>Background</h3><p>The prognosis and survival of patients with lung cancer are likely to deteriorate with metastasis. Using deep-learning in the detection of lymph node metastasis can facilitate the noninvasive calculation of the likelihood of such metastasis, thereby providing clinicians with crucial information to enhance diagnostic precision and ultimately improve patient survival and prognosis</p></div><div><h3>Methods</h3><p>In total, 623 eligible patients were recruited from two medical institutions. Seven deep learning models, namely Alex, GoogLeNet, Resnet18, Resnet101, Vgg16, Vgg19, and MobileNetv3 (small), were utilized to extract deep image histological features. The dimensionality of the extracted features was then reduced using the Spearman correlation coefficient (r ≥ 0.9) and Least Absolute Shrinkage and Selection Operator. Eleven machine learning methods, namely Support Vector Machine, K-nearest neighbor, Random Forest, Extra Trees, XGBoost, LightGBM, Naive Bayes, AdaBoost, Gradient Boosting Decision Tree, Linear Regression, and Multilayer Perceptron, were employed to construct classification prediction models for the filtered final features. The diagnostic performances of the models were assessed using various metrics, including accuracy, area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value. Calibration and decision-curve analyses were also performed.</p></div><div><h3>Results</h3><p>The present study demonstrated that using deep radiomic features extracted from Vgg16, in conjunction with a prediction model constructed via a linear regression algorithm, effectively distinguished the status of mediastinal lymph nodes in patients with lung cancer. The performance of the model was evaluated based on various metrics, including accuracy, area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value, which yielded values of 0.808, 0.834, 0.851, 0.745, 0.829, and 0.776, respectively. The validation set of the model was assessed using clinical decision curves, calibration curves, and confusion matrices, which collectively demonstrated the model's stability and accuracy</p></div><div><h3>Conclusion</h3><p>In this study, information on the deep radiomics of Vgg16 was obtained from computed tomography images, and the linear regression method was able to accurately diagnose mediastinal lymph node metastases in patients with lung cancer.</p></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096579623000463/pdfft?md5=d355b811e3e99356748d10c345ee1b33&pid=1-s2.0-S2096579623000463-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141484841","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":"Trust in ESG reporting: The intelligent Veri-Green solution for incentivized verification","authors":"Liyuan Liu ,&nbsp;Zhiguo Ma ,&nbsp;Yiyun Zhou ,&nbsp;Melissa Fan ,&nbsp;Meng Han","doi":"10.1016/j.bcra.2024.100189","DOIUrl":"10.1016/j.bcra.2024.100189","url":null,"abstract":"<div><p>In today's corporate environment, Environmental, Social, and Governance (ESG) reports crucially reflect an organization's commitment to sustainability, environmental preservation, and social responsibility. As corporations share these detailed reports, the responsibility to validate and assure adherence to respected ESG benchmarks critically lies with third-party assurance organizations. However, the essential verification process often encounters challenges related to authenticity, credibility, and fairness, underscoring the need for a new solution. The selection of verifiers is a crucial aspect of this process, as their expertise and impartiality directly impact the validity and trustworthiness of the verification. Consequently, “Veri-Green,” an innovative blockchain-based incentive mechanism, has been introduced to improve the ESG data verification process. Considering potential risks in verification systems, such as reputational damage due to oversight or inadvertent approval of inaccurate data, and data security risks involving the management of sensitive organizational information, the verifier selection process needs to be thoroughly considered and designed. Through the utilization of advanced machine learning algorithms, potential verification candidates are precisely identified, followed by the deployment of the Vickrey Clarke Groves (VCG) auction mechanism. This approach ensures the strategic selection of verifiers and cultivates an ecosystem marked by truthfulness, rationality, and computational efficiency throughout the ESG data verification process. In this framework, verifiers are not only encouraged but also properly incentivized, developing a more transparent and equitable verification process, thereby driving the ESG agenda towards a future defined by genuine, impactful corporate responsibility and sustainability.</p></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096720924000022/pdfft?md5=a05aa881600d205edb9fd810828ad931&pid=1-s2.0-S2096720924000022-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140520791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"United Nations side event on the Biological Weapons Convention by Tianjin University and City, University of London","authors":"","doi":"10.1016/j.jobb.2024.06.001","DOIUrl":"https://doi.org/10.1016/j.jobb.2024.06.001","url":null,"abstract":"","PeriodicalId":52875,"journal":{"name":"Journal of Biosafety and Biosecurity","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2588933824000220/pdfft?md5=e6383a2cb6198e811a9779c39a386705&pid=1-s2.0-S2588933824000220-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141314734","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":"Intelligent diagnosis of atrial septal defect in children using echocardiography with deep learning","authors":"Yiman LIU ,&nbsp;Size HOU ,&nbsp;Xiaoxiang HAN ,&nbsp;Tongtong LIANG ,&nbsp;Menghan HU ,&nbsp;Xin WANG ,&nbsp;Wei GU ,&nbsp;Yuqi ZHANG ,&nbsp;Qingli LI ,&nbsp;Jiangang CHEN","doi":"10.1016/j.vrih.2023.05.002","DOIUrl":"https://doi.org/10.1016/j.vrih.2023.05.002","url":null,"abstract":"<div><h3>Background</h3><p>Atrial septal defect (ASD) is one of the most common congenital heart diseases. The diagnosis of ASD via transthoracic echocardiography is subjective and time-consuming.</p></div><div><h3>Methods</h3><p>The objective of this study was to evaluate the feasibility and accuracy of automatic detection of ASD in children based on color Doppler echocardiographic static images using end-to-end convolutional neural networks. The proposed depthwise separable convolution model identifies ASDs with static color Doppler images in a standard view. Among the standard views, we selected two echocardiographic views, i.e., the subcostal sagittal view of the atrium septum and the low parasternal four-chamber view. The developed ASD detection system was validated using a training set consisting of 396 echocardiographic images corresponding to 198 cases. Additionally, an independent test dataset of 112 images corresponding to 56 cases was used, including 101 cases with ASDs and 153 cases with normal hearts.</p></div><div><h3>Results</h3><p>The average area under the receiver operating characteristic curve, recall, precision, specificity, F1-score, and accuracy of the proposed ASD detection model were 91.99, 80.00, 82.22, 87.50, 79.57, and 83.04, respectively.</p></div><div><h3>Conclusions</h3><p>The proposed model can accurately and automatically identify ASD, providing a strong foundation for the intelligent diagnosis of congenital heart diseases.</p></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096579623000244/pdfft?md5=3ade0d91e713f6555fd1c75181120add&pid=1-s2.0-S2096579623000244-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141484840","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 Novel Recommendation Algorithm Integrates Resource Allocation and Resource Transfer in Weighted Bipartite Network","authors":"Qiang Sun, Leilei Shi, Lu Liu, Zi-xuan Han, Liang Jiang, Yan Wu, Yeling Zhao","doi":"10.26599/bdma.2023.9020029","DOIUrl":"https://doi.org/10.26599/bdma.2023.9020029","url":null,"abstract":"","PeriodicalId":52355,"journal":{"name":"Big Data Mining and Analytics","volume":null,"pages":null},"PeriodicalIF":13.6,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141229821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chi-square automatic interaction detection (CHAID) analysis of the use of safety goggles and face masks as personal protective equipment (PPE) to protect against occupational biohazards","authors":"Raúl Aguilar-Elena,&nbsp;Juán José Agún-González","doi":"10.1016/j.jobb.2024.05.001","DOIUrl":"10.1016/j.jobb.2024.05.001","url":null,"abstract":"<div><h3>Background</h3><p>This study represents the first Spanish investigation to rigorously evaluate compliance with the use of safety goggles and face masks as essential personal protective equipment (PPE) in companies with workplaces involving exposure to biological agents.</p></div><div><h3>Objectives</h3><p>This study aimed to examine the degree of use of face masks and safety goggles as personal protective equipment (PPE), the factors that influence their use, and the profile of workers exposed to occupational biological agents in Spanish companies in the health sector, farming sector, meat industry, waste treatment plants, food industry, and veterinary centers.</p></div><div><h3>Methods</h3><p>We conducted a cross-sectional descriptive study involving 590 Spanish workers from 51 companies. We developed a 34-item questionnaire to assess workers’ perception of risk related to exposure to biological agents in their workplaces. Among the questions, three were designed to measure the degree of use of key protective equipment in sectors with biological agent exposure: protective gloves, safety goggles or face masks. We only analyzed safety goggles and face masks. We performed various statistical analyses, including Cronbach’s alpha, frequency of endorsement, content validity ratio using Lawshe’s method, varimax rotation, the Kaiser-Meyer-Olkin test, and Bartlett’s sphericity test, to assess the internal consistency and reliability of the questionnaire. Additionally, we employed a chi-square automatic interaction detection (CHAID) segmentation analysis, using workers’ responses regarding their attitudes toward safety goggles and face mask usage as PPE for protection against biological risks, with demographic variables as independent factors.</p></div><div><h3>Results</h3><p>In the current study, CHAID analysis revealed that workers exposed to group 2 biological agents used more safety goggles and face shields compared with workers exposed to other groups of biological agents. Moreover, workers in laboratories and the food industry used face masks more than workers of other sectors.</p></div><div><h3>Conclusion</h3><p>The CHAID analysis in the current study indicated that workers exposed to biological agents from both group 2 and group 3 demonstrated satisfactory levels of compliance and utilization of protective masks, surpassing their counterparts in terms of usage. Workers in the food and laboratory industries had subpar compliance with preventive measures, and employees from companies with internal health and safety departments exhibited significant adherence to workplace mask usage, safeguarding themselves against biological risks.</p></div>","PeriodicalId":52875,"journal":{"name":"Journal of Biosafety and Biosecurity","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2588933824000190/pdfft?md5=4e6d1b822442a2758e44cf734863021f&pid=1-s2.0-S2588933824000190-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141145411","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":"Variational autoencoder-based techniques for a streamlined cross-topology modeling and optimization workflow in electrical drives","authors":"Marius Benkert,&nbsp;Michael Heroth,&nbsp;Rainer Herrler,&nbsp;Magda Gregorová,&nbsp;Helmut C. Schmid","doi":"10.1007/s43684-024-00065-x","DOIUrl":"10.1007/s43684-024-00065-x","url":null,"abstract":"<div><p>The generation and optimization of simulation data for electrical machines remain challenging, largely due to the complexities of magneto-static finite element analysis. Traditional methodologies are not only resource-intensive, but also time-consuming. Deep learning models can be used to shortcut these calculations. However, challenges arise when considering the unique parameter sets specific to each machine topology. Building on two recent studies (Parekh et al. in IEEE Trans. Magn. 58(9):1–4, 2022; Parekh et al., Deep learning based meta-modeling for multi-objective technology optimization of electrical machines, 2023, arXiv:2306.09087), that utilized a variational autoencoder to cohesively map diverse topologies into a singular latent space for subsequent optimization, this paper proposes a refined architecture and optimization workflow. Our modifications aim to streamline and enhance the robustness of both the training and optimization processes, and compare the results with the variational autoencoder architecture proposed recently.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00065-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142413467","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":"Distant supervision knowledge extraction and knowledge graph construction method for supply chain management domain","authors":"Feiyue Huang,&nbsp;Lianglun Cheng","doi":"10.1007/s43684-024-00064-y","DOIUrl":"10.1007/s43684-024-00064-y","url":null,"abstract":"<div><p>As the core competitiveness of the national industry, large-scale equipment such as ships, high-speed rail and nuclear power equipment, their production process involves in-depth personalization. It includes complex processes and long manufacturing cycles. In addition, the equipment’s supply chain management is extremely complex. Therefore, the development of a supply chain management knowledge graph is of significant strategic significance. It not only enhances the synergistic effect of the supply chain management but also upgrades the level of intelligent management. This paper proposes a distant supervision knowledge extraction and knowledge graph construction method in the supply chain management of large equipment manufacturing, which achieves digital and structured management and efficient use of supply chain management knowledge in the industry. This paper presents an approach to extract entity-relation knowledge using limited samples. We achieve this by establishing a distant supervision model. Furthermore, we introduce a fusion gate mechanism and integrate ontology information, thereby enhancing the model’s capability to effectively discern sentence-level semantics. Subsequently, we promptly modify the weights of input features using the gate mechanism to strengthen the model’s resilience and address the issue of vector noise diffusion. Finally, an inter-bag sentence attention mechanism is introduced to integrate different sentence bag information at the sentence bag level, which achieves more accurate entity-relation knowledge extraction. The experimental results prove that compared with the latest distant supervision method, the accuracy of relation extraction is improved by 2.8%, and the AUC value is increased by 3.9%, effectively improving the quality of knowledge graph in supply chain management.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00064-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141112358","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}