检索结果-内蒙古大学图书馆

2023 IEEE Zooming Innovation in Consumer Technologies Conference, ZINC 2023

作者： Chalas, Filip Stupavsky, Igor Vranic, Valentino Slovak University of Technology in Bratislava Faculty of Informatics and Information Technologies Institute of Informatics Information Systems and Software Engineering Slovakia

ISBN: (纸本)9798350347722

The amount of antisocial online behavior (AOB) in the political field has been on the rise in recent years. In the research, we are dealing with the reason for confusing AOB with other forms of antisocial behavior. We specifically dealt with NLP in the political field in the Slovak language, with a specific number of prefixes and suffixes. In this paper, we focus on the area of detection of manipulation of political discussions. The data source consisted of previously collected data in the period from April 2018 with an update until November 19, 2022 realized within this research from the site ***, which we supplemented with new claims. We trained several classification models on preprocessed data and evaluated them. For multi-class classification, the best results were achieved using logistic regression and a support vector machine trained on the resampled dataset-both achieving an accuracy of 0.56 and a macro F1 score of 0.39. In the case of binary classification, the best results were achieved by logistic regression-accuracy 0.7 and macro F1 score 0.56. These models could help detect manipulation in online political discussions. © 2023 IEEE.

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

Sentence Compression as a Supervised Learning with a Rich Feature Space 19th

Sentence Compression as a Supervised Learning with a Ric...

引用

19th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2018

作者： Churkin, Elena Last, Mark Litvak, Marina Vanetik, Natalia Department of Software Engineering Shamoon Academic College Beer Sheva Israel Department of Software and Information Systems Engineering Ben Gurion University of the Negev Beer Sheva Israel

ISBN: (纸本)9783031238031

We present a novel supervised approach to sentence compression, based on classification and removal of word sequences generated from subtrees of the original sentence dependency tree. Our system may use any known classifier like Support Vector Machines or Logistic Model Tree to identify word sequences that can be removed without compromising the grammatical correctness of the compressed sentence. We trained our system using several classifiers on a small annotated dataset of 100 sentences, which included around 1500 manually labeled subtrees (removal candidates) represented by 25 features. The highest cross-validation classification accuracy of 80% was obtained with the SMO (Normalized Poly Kernel) algorithm. We evaluated the readability and the informativeness of the sentences compressed by the SMO-based classification model with the help of human raters using a separate benchmark dataset of 200 sentences. © 2023, Springer Nature Switzerland AG.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Multi-layer composite autoencoders for semi-supervised change detection in heterogeneous remote sensing images

引用

Science China(information Sciences) 2023年第4期66卷 124-125页

作者： Jiao SHI Tiancheng WU Hanwen YU A.K.QIN Gwanggil JEON Yu LEI School of Electronics and Information Northwestern Polytechnical University School of Resources and Environment University of Electronic Science and Technology of China Department of Computer Science and Software Engineering Swinburne University of Technology Department of Embedded Systems Engineering Incheon National University

With the increasing complexity of application scenarios, the fusion of different remote sensing data types has gradually become a trend, which can greatly improve the utilization of massive remote sensing *** the problem of change detection for heterogeneous remote images can be much more complicated than the traditional change detection for homologous remote sensing images,

关键词：

来源：评论

学校读者我要写书评

暂无评论

software Defect Prediction Using Ensemble Technique 9

Software Defect Prediction Using Ensemble Technique

引用

9th International Conference on information Management and Technology, ICIMTech 2024

作者： Wicaksono, Pandu Arisaputra, Panji Yunanda, Rezky Kristian, Willy Mujhid, Almuzhidul School of Computer Science Bina Nusantara University Software Engineering Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

software defect prediction is the methodical process of identifying code segments that are likely to have problems. This is done by analyzing software metrics and using categorization algorithms. This work introduces an alternate approach that utilizes ensemble learning techniques to improve the effectiveness of fault detection models. The models utilized in this study are CodeGPT and CodeBERT, both of which are transformer-based deep learning models that can extract code features from source code. The models are trained using the PROMISE dataset, which consists of Java projects that have been labeled with defects. The test results show that using ensemble learning methods can improve the accuracy, precision, recall, and F1 score of a single model by around 1-3%. This study contributes to the progress of software dependability and quality by utilizing advanced software fault prediction algorithms. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

引用

Computer Modeling in engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

来源：评论

学校读者我要写书评

暂无评论

Using Machine Learning Models for Earthquake Magnitude Prediction in California, Japan, and Israel 1

引用

7th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2023

作者： Novick, Deborah Last, Mark Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva84105 Israel

ISBN: (数字)9783031346712

ISBN: (纸本)9783031346705

This study aims at predicting whether an earthquake of magnitude greater than the regional median of maximum yearly magnitudes will occur during the next year. Prediction is performed by training various machine learning algorithms, such as AdaBoost, XGBoost, Random Forest, Logistic Regression, and Info-Fuzzy Network. The models are induced using a combination of seismic indicators used in the earthquake literature as well as various time-series features, such as features based on the moving averages of the number of earthquakes in each area, features that record the number of events above and below the mean in a time period, and features based on lagged values of the mean and median magnitude. Feature selection is performed using a forward search algorithm that chooses the most effective features for prediction. The models are trained and evaluated using earthquake catalog data obtained for California, Japan, and Israel. In addition, models trained on either California or Japan datasets are evaluated using the remaining data. Models trained on Japan data achieve AUC scores up to 0.825;models trained on California data achieve AUC scores up to 0.738;and models trained on Israel data achieve AUC scores up to 0.710. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Earthquakes

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Camera Calibration Algorithms for Football Applications 4

Comparative Analysis of Camera Calibration Algorithms for Fo...

引用

4th International Workshop on information Technologies: Theoretical and Applied Problems, ITTAP 2024

作者： Sorokivskyi, Oleksandr Hotovych, Volodymyr Nazarevych, Oleg Shymchuk, Grygorii Department of Computer Science Faculty of Computer Information Systems and Software Engineering Ternopil Ivan Puluj National Technical University Ternopil Ukraine

In solving the problem of automated analysis of football match video recordings, special video cameras are currently used. This work presents a comparative characterization of known algorithms and methods for video camera calibration, including those utilizing machine learning and neural networks, with the aim of identifying their shortcomings and forming a theoretical foundation for developing modern, more effective methods and algorithms. Specifically, it examines both algorithms that require more input data but operate quickly [1, 2] and more accurate algorithms using machine learning [3, 4, 5, 6, 5]. It is demonstrated that their main drawback is either accuracy or speed. More accurate algorithms using machine learning often do not specify the algorithm's operational speed, which precludes their use in real-time applications. The examined works that emphasize speed frequently lack the accuracy necessary for practical use in real-life scenarios. Copyright © 2024 for this paper by its authors.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Research on Financial Statement Fraud Detection Method Based on Support Vector Machine 1

Research on Financial Statement Fraud Detection Method Based...

引用

1st International Conference on Intelligent Manufacturing and Cloud Computing, ICIMCC 2024

作者： Yan, Yuan Wang, Zhenyu Zhang, Shuai Information Management and Information Systems Southwest Jiaotong University Sichuan Chengdu610031 China Software Engineering Beihang University Beijing Beijing100191 China Baoding071003 China

ISBN: (纸本)9781643685779

In order to improve the efficiency of financial statement fraud detection, a Support Vector Machine (SVM) model was adopted, combined with feature selection and dimensionality reduction techniques, to analyze the financial data of 200 listed companies. The results showed that the SVM model achieved an accuracy rate of 92.5%, which further increased to 94.1% after integrating a neural network, significantly enhancing the ability to identify fraudulent activities. The findings provide important technical support for optimizing financial risk management and contribute to improving market transparency and corporate integrity. © 2025 The Authors.

关键词： Decentralized finance

来源：评论

学校读者我要写书评

暂无评论

Implementation and Evaluation of Equitable Practices in an Introductory CS Course 2024

Implementation and Evaluation of Equitable Practices in an I...

引用

55th ACM Technical Symposium on Computer Science Education, SIGCSE 2024

作者： Najjar, Nadia Basu, Debarati Software and Information Systems University of North Carolina Charlotte United States Engineering Fundamentals Embry-Riddle Aeronautical University United States

ISBN: (纸本)9798400704246

Lack of diversity is a major challenge in CS education. One way to broaden participation and increase student success is the adoption of equitable pedagogical and grading practices. Lack of evaluation of such practices within the context of undergraduate CS education results in less adoption. This study is the first step in a broader project examining such practices and their impact on undergraduate CS students. Factors such as confidence and sense-of-belonging have been linked to increase students' success. In this study, we report on the evaluation of four practices commonly adopted in pursuit of equity, in terms of students' perceptions and how they impact students' sense-of-belonging, confidence, and performance in a computer science course. We report on students' feedback from a CS2 course in the Spring 2023 semester. Our results show that students valued these practices and perceived them as having a positive impact on their course experience in relation to these constructs. Students also reported that these practices gave them better control of their learning, allowed them to learn from their mistakes, and have a better understanding of the course content. © 2024 Owner/Author.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images

引用

Computers, Materials & Continua 2023年第7期76卷 133-150页

作者： Ali Haider Khan Hassaan Malik Wajeeha Khalil Sayyid Kamran Hussain Tayyaba Anees Muzammil Hussain Department of Software Engineering Faculty of Computer SciencesLahore Garrison UniversityLahorePakistan Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan Department of Computer Science&Information Technology University of Engineering and Technology(UET)PeshawarPakistan Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan

To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated *** fundus imaging(CFI)is a screening technology that is both effective and *** to CFIs,the early stages of the disease are characterized by a paucity of observable symptoms,which necessitates the prompt creation of automated and robust diagnostic *** traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of *** addition,they usually only target one or a few different kinds of eye diseases at the same *** this study,we design a patient-level multi-label OD(PLML_ODs)classification model that is based on a spatial correlation network(SCNet).This model takes into consideration the relevance of patient-level diagnosis combining bilateral eyes and multi-label ODs ***_ODs is made up of three parts:a backbone convolutional neural network(CNN)for feature extraction i.e.,DenseNet-169,a SCNet for feature correlation,and a classifier for the development of classification *** DenseNet-169 is responsible for retrieving two separate sets of attributes,one from each of the left and right *** then,the SCNet will record the correlations between the two feature sets on a pixel-by-pixel *** the attributes have been analyzed,they are integrated to provide a representation at the patient *** the whole process of ODs categorization,the patient-level representation will be *** efficacy of the PLML_ODs is examined using a soft margin loss on a dataset that is readily accessible to the public,and the results reveal that the classification performance is significantly improved when compared to several baseline approaches.

关键词： Ocular disease multi-label spatial correlation CNN eye disease

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：