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Proceedings of the 41st International Conference on Machine Learning

作者： Baohong Li Anpeng Wu Ruoxuan Xiong Kun Kuang College of Computer Science and Technology Zhejiang University Hangzhou China Department of Quantitative Theory & Methods Emory University Atlanta

Latent confounding bias and collider bias are two key challenges of causal inference in observational studies. Latent confounding bias occurs when failing to control the unmeasured covariates that are common causes of treatments and outcomes, which can be addressed by using the Instrumental Variable (IV) approach. Collider bias comes from non-random sample selection caused by both treatments and outcomes, which can be addressed by using a different type of instruments, i.e., shadow variables. However, in most scenarios, these two biases simultaneously exist in observational data, and the previous methods focusing on either one are inadequate. To the best of our knowledge, no approach has been developed for causal inference when both biases exist. In this paper, we propose a novel IV approach, Two-Stage Shadow Inclusion (2SSI), which can simultaneously address latent confounding bias and collider bias by utilizing the residual of the treatment as a shadow variable. Extensive experimental results on benchmark synthetic datasets and a real-world dataset show that 2SSI achieves noticeable performance improvement when both biases exist compared to existing methods.

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Application for Statistical Processing of Pedagogical Experiment Results: A Component-Based Approach

Application for Statistical Processing of Pedagogical Experi...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Vitalii M. Bazurin Oleg I. Pursky Olga S. Chashechnikova Department of Computer Science and Information Systems State University of Trade and Economics Kyiv Ukraine Department of Mathematics Physics and Teaching Methods Sumy State Pedagogical University named after A.S.Makarenko Sumy Ukraine

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Pedagogical scientists often need to process the results of a pedagogical experiment. The purpose of the article is to reveal the features of the architecture and structure of the application for statistical processing of the results of a pedagogical experiment. To statistically process the results of a pedagogical experiment, we have developed an application that provides the calculation of Mann-Whitney, Pearson, and Student criteria. For the application, we chose the modular architecture, which is a hierarchical structure. Due to the component-oriented approach, it is possible to modernize and expand the application's functionality. The application can read data from a text file, an XLSX file, a database, or a table in the main form of the application.

关键词： Automation Accuracy Databases Soft sensors computer architecture

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Automatic Enrichment of Abstract Meaning Representations 16

Automatic Enrichment of Abstract Meaning Representations

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16th Linguistic Annotation Workshop, LAW 2022

作者： Ji, Yuxin Williamson, Gregor Choi, Jinho D. Department of Quantitative Theory and Methods Emory University AtlantaGA30322 United States Department of Computer Science Emory University AtlantaGA30322 United States

ISBN: (纸本)9782493814081

Meaning Representation (AMR) is a semantic graph framework which inadequately represent a number of important semantic features including number, (in)definiteness, quantifiers, and intensional contexts. Several proposals have been made to improve the representational adequacy of AMR by enriching its graph structure. However, these modifications are rarely added to existing AMR corpora due to the labor costs associated with manual annotation. In this paper, we develop an automated annotation tool which algorithmically enriches AMR graphs to better represent number, (in)definite articles, quantificational determiners, and intensional arguments. We compare our automatically produced annotations to gold-standard manual annotations and show that our automatic annotator achieves impressive results. © 2022 European Language Resources Association (ELRA).

关键词： Wages

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PREDICTING ACCIDENT SEVERITY: AN ANALYSIS OF FACTORS AFFECTING ACCIDENT SEVERITY USING RANDOM FOREST MODEL

arXiv

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arXiv 2023年

作者： Adefabi, Adekunle Olisah, Somtobe Obunadike, Callistus Oyetubo, Oluwatosin Taiwo, Esther Tella, Edward Department of Computer Science and Quantitative Methods Austin Peay State University TN United States

Road accidents have significant economic and societal costs, with a small number of severe accidents accounting for a large portion of these costs. Predicting accident severity can help in the proactive approach to road safety by identifying potential unsafe road conditions and taking well-informed actions to reduce the number of severe accidents. This study investigates the effectiveness of the Random Forest machine learning algorithm for predicting the severity of an accident. The model is trained on a dataset of accident records from a large metropolitan area and evaluated using various metrics. Hyperparameters and feature selection are optimized to improve the model's performance. The results show that the Random Forest model is an effective tool for predicting accident severity with an accuracy of over 80%. The study also identifies the top six most important variables in the model, which include wind speed, pressure, humidity, visibility, clear conditions, and cloud cover. The fitted model has an Area Under the Curve of 80%, a recall of 79.2%, a precision of 97.1%, and an F1 score of 87.3%. These results suggest that the proposed model has higher performance in explaining the target variable, which is the accident severity class. Overall, the study provides evidence that the Random Forest model is a viable and reliable tool for predicting accident severity and can be used to help reduce the number of fatalities and injuries due to road accidents in the United States. © 2023, CC0.

关键词： Forecasting

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Integrating machine learning and human use experience to identify personalized pharmacotherapy in Traditional Chinese Medicine:a case study on resistant hypertension

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Journal of Traditional Chinese Medicine 2025年第1期45卷 192-200页

作者： CHE Qianzi LIU Dasheng XIANG Xinghua TIAN Yaxin XIE Feibiao XU Wenyuan LIU Jian WANG Xuejie WANG Liying BAI Weiguo HAN Xuejie YANG Wei Department of Medical Statistics Institute of Basic Research in Clinical MedicineChina Academy of Chinese Medical SciencesBeijing 100700China Department of Science and Education Medical Statistics Teaching and Research OfficeInstitute of Basic Research in Clinical MedicineChina Academy of Chinese Medical SciencesBeijing 100700China Traditional Chinese Medicine Standards Research Center Medical Statistics Teaching and Research OfficeInstitute of Basic Research in Clinical MedicineChina Academy of Chinese Medical SciencesBeijing 100700China Computer Department Xiyuan Hospital of the China Academy of Chinese Medical SciencesBeijing 100091China

OBJECTIVE:To enhance the understanding of identifying personalized pharmacotherapy options in Traditional Chinese Medicine(TCM),and further support the registration of new TCM ***:Generalized Boosted Models and XGBoost were employed to construct a classification model to identify the bad prognosis factors in resistant hypertension(RH)***,we used association analysis to explore the rules of"symptomsyndrome"and"symptom-herb"for the major influencing factors,in order to summarize prescription pattern and applicable patients of ***:Patients with major adverse cardiac events mostly have complex symptoms of phlegm,stasis,deficiency and fire intermingled with each other,and finally summarized the human experience of using Chinese herbal medicine to precisely intervene in some symptoms of RH patients on the basis of conventional Western medical ***:Machine learning algorithms can make full use of human use experience and evidence to save clinical trial resources and accelerate the development of TCM varieties.

关键词： machine learning human clinical experience personalized pharmacotherapy new drugs registration

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Optimal Deep Hybrid Boltzmann Machine Based Arabic Corpus Classification Model

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computer Systems science & Engineering 2023年第9期46卷 2755-2772页

作者： Mesfer Al Duhayyim Badriyya B.Al-onazi Mohamed K.Nour Ayman Yafoz Amal S.Mehanna Ishfaq Yaseen Amgad Atta Abdelmageed Gouse Pasha Mohammed Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah 24211Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Natural Language Processing(NLP)for the Arabic language has gained much significance in recent *** most commonly-utilized NLP task is the‘Text Classification’*** main intention is to apply the Machine Learning(ML)approaches for automatically classifying the textual files into one or more pre-defined *** ML approaches,the first and foremost crucial step is identifying an appropriate large dataset to test and train the *** of the trending ML techniques,i.e.,Deep Learning(DL)technique needs huge volumes of different types of datasets for training to yield the best *** current study designs a new Dice Optimization with a Deep Hybrid Boltzmann Machinebased Arabic Corpus Classification(DODHBM-ACC)model in this *** presented DODHBM-ACC model primarily relies upon different stages of pre-processing and the word2vec word embedding *** Arabic text classification,the DHBM technique is *** technique is a hybrid version of the Deep Boltzmann Machine(DBM)and Deep Belief Network(DBN).It has the advantage of learning the decisive intention of the classification *** adjust the hyperparameters of the DHBM technique,the Dice Optimization Algorithm(DOA)is exploited in this *** experimental analysis was conducted to establish the superior performance of the proposed DODHBM-ACC *** outcomes inferred the better performance of the proposed DODHBM-ACC model over other recent approaches.

关键词： Arabic corpus text classification machine learning deep learning dice optimization

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Novel vision transformer and data augmentation technique for efficient detection of monkeypox disease

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Multimedia Tools and Applications 2024年 1-19页

作者： Alarfaj, Aisha Ahmed Ahmad, Salman Hakeem, Abeer M. Alabdulqader, Ebtisam Abdullah PERO, Chiara Alsubai, Shtwai Innab, Nisreen Ashraf, Imran Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Post Graduate Resident Department of Urology Allama Iqbal Teaching Hospital Dera Ghazi Khan Pakistan Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Technology College of Computer and Information Sciences King Saud University P. O. Box 800 Riyadh11421 Saudi Arabia Department of Management & amp Innovation Systems University of Salerno Via Giovanni Paolo II 132 Fisciano Salerno Italy Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Diriyah Riyadh13713 Saudi Arabia Department of Information and Communication Engineering Yeungnam University Gyeongsan Korea Republic of

Recent technological advancements have paved the way for the optimization of medical processes, particularly automated disease detection. Moreover, the adoption of machine learning (ML) has greatly helped in automating disease detection. Such approaches can detect various diseases early, enabling timely treatment to save countless lives. Early and accurate diagnosis is very important for diseases like monkeypox, to curb its spread. Monkeypox is a viral disease caused by double-stranded DNA and can be transmitted through close contact with infected humans or animals. It’s early identification and accurate lesion diagnosis are critical to contain the disease. This study proposes an automated approach to optimize the diagnosis of monkeypox disease using a novel vision transformer, which is utilized due to its effectiveness for feature extraction. The Proposed approach’s efficiency and accuracy are tested on a public benchmark dataset comprising a variety of skin lesions of different ages and genders. In addition, data augmentation involves rotation, scaling, and flipping thereby enhancing the density of the training data set for better generalization of ML models. Experiments involve binary, as well as, multi-class classification. For the binary class, the proposed model achieves an accuracy of 97.63%, outperforming traditional ML and deep learning (DL) techniques. In the case of multi-class classification with monkeypox, measles, normal, HFMD, cowpox, and chickenpox classes, the proposed model archives an accuracy of 90.61% while precision, recall, and F1 scores are 91.39%, 89.17%, and 90.28%, respectively. Furthermore, the proposed approach shows average accuracy, precision, recall, and F1 scores of 97.54%, 96.19%, 95.16%, and 95.67%, respectively for five-fold cross-validation. Experiments demonstrate that the combination of data augmentation techniques and the vision transformer model significantly optimizes diagnostic performance. In brief, advanced DL architectur

关键词： Medical image processing

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Research on Online Mathematics teaching Resource Integration Model Based on Deep Neural Network 8th

Research on Online Mathematics Teaching Resource Integration...

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8th EAI International Conference on e-Learning, e-Education, and Online Training, eLEOT 2022

作者： Wang, Yue Zhang, Yiqian Division of Public Teaching Jinan Vocational College Jinan250103 China Department of Computer Science Jinan Vocational College Jinan250103 China

ISBN: (纸本)9783031211638

Improving the quality of resource integration is one of the effective ways to improve the utilization of teaching resources. Therefore, aiming at the online mathematics course, this research uses the deep neural network as the basic means to carry out a new design of the teaching resource integration model. First, the overall design is carried out, including the model architecture, resource data transmission mode, data management mode, model function design and cloud platform configuration. Then, using the elastic computing technology in the field of deep neural network technology, based on the collection of relevant resources, the cloud storage and management of resources are implemented with the mathematics course courseware as the core and the extensible software as the support. Finally, in the deep neural network, the dynamically expandable and virtualized storage resources are used to provide the storage and access services of teaching resources, and the integration effect is improved by classifying the resource categories when uploading resources. Compared with the traditional model, the results show that the characteristics of this model are more prominent, and the application advantages in accuracy and resource reading speed are obvious. © 2022, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Deep neural networks

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Data Optimization Query Method of Online Education System Based on Decision Tree 8th

Data Optimization Query Method of Online Education System Ba...

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8th EAI International Conference on e-Learning, e-Education, and Online Training, eLEOT 2022

作者： Zhang, Yiqian Wang, Yue Department of Computer Science Jinan Vocational College Jinan250103 China Division of Public Teaching Jinan Vocational College Jinan250103 China

ISBN: (纸本)9783031211638

The traditional online education system data query method has the problem of unclear track data type, which leads to a long query time. To solve this problem, this study designs a data optimization query method of online education system based on decision tree. According to the teaching objectives of teachers and the characteristics of courses, extract the course characteristics of online education system, and adjust the data segmentation process, so as to extract the grid memory query index structure. Then select a field as the segmentation basis and mark, and use the decision tree to identify the trajectory data type. Finally, set the data optimization query mode by continuously predefined query interval. The experimental results show that compared with the other two query methods, the query time of this method is less, which shows that the application performance of the data optimization query method of online education system integrated with decision tree is better. © 2022, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Decision trees

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The Reliability of Information Sources: Student Perceptions and Educational Opportunities 7

The Reliability of Information Sources: Student Perceptions ...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power Engineering, CANDO-EPE 2024

作者： Kollar, Anita Mihalovicsne Katona, Jozsef Obuda University Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemet Hungary Budapest Business University Faculty of Finance and Accountancy Department of Applied Quantitative Methods Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary

ISBN: (纸本)9798331531119

The modern digital age demands the skills to be able to assess information. In the current world, access to information and knowledge is abundant from different sources. Indeed, high-quality data are much needed in education and professional development, as the quality and accuracy of information directly influence the process of learning and decision-making. The study aims to investigate the perceptions of computer science students at the University of Dunaújváros related to the credibility of diverse information sources. Within a questionnaire, on a voluntary answer basis, 68 students took part, 59 males and nine females. This work is aimed at assessing the degree to which the surveyed students evaluate information sources based on a five-point Likert scale. The data shows that students find print books and textbooks as the most reliable sources;social media are the least reliable. The study also supports the importance of source credibility evaluation, for which lateral reading techniques and AI-based news source credibility identification systems can be helpful. These findings suggest the necessity of bettering the educational approaches to foster better source criticism skills in the students, especially for online content and social media. The study generally contributes to understanding the information society and helps prepare students to obtain and use reliable and accurate information in their future careers. Using AI-based tools and systems can then further support these educational efforts with robust mechanisms to evaluate information credibility in an ever more complex digital landscape. © 2024 IEEE.

关键词： Data reliability

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