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共 37040 条记录，以下是4591-4600 订阅

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Automatic Detection of Learner’s Learning Style 4th

Automatic Detection of Learner’s Learning Style

引用

4th international conference on data, engineering, and Applications, IDEA 2022

作者： Sharuni, S. Dhana Lakshmi, R. Murugappan, Abirami Department of Information Science and Technology College of Engineering Guindy Anna University Chennai India

ISBN: (纸本)9789819700363

Learning style is crucial in assisting students in better understanding things learnt and in helping them retain them for extended periods of time. Utilising surveys, learning styles in traditional and online environments are identified. To automatically determine a learner’s preferred learning style without upsetting them, it is becoming more popular. Machine learning is a branch of artificial intelligence that allows systems to learn and develop without being explicitly programmed. A learning style is the way that makes one learns best. It is based on the tastes and traits of the individual. Because different students learn in different ways, individual learning styles are vital to consider in effective teaching. Learning style is important in assisting students in remembering topics for extended periods of time and improving their knowledge of concepts. The way that kids learn is crucial in helping them retain information for longer periods of time and advance their conceptual understanding. Surveys are used to determine learning preferences both offline and online. The goal is to recognise and apply characteristics that will automatically and unobtrusively determine a learner’s preferred learning style. Using Multi-nominal Naive Bayes the data acquired from students in various classes is used to train a model, which may then be used to predict a student’s learning style based on the suggested traits. The information gathered from learners is used to trained model, and it could be used for appropriately predict the learner’s learning style based on the recommended qualities. The accuracy in each dimension for the different machine learning algorithms was calculated using the data obtained for the VARK model (Visual, Aural, Read/Write, and Kinesthetic). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Contrastive Learning

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English Tutoring Learning System Based on Machine Learning Algorithm

English Tutoring Learning System Based on Machine Learning A...

引用

2022 IEEE international conference on knowledge engineering and Communication systems, ICKES 2022

作者： Liu, Shiyu Heilongjiang International University Heilongjiang Harbin150050 China

ISBN: (纸本)9781665456371

With the development of multimedia computer technology, media such as text, images, and sounds have provided unprecedented conditions for students to learn online. With the help of big data and artificial intelligence technology, online education has become an important way of education. It is also a hot area of research in many domestic and foreign universities and enterprises. The purpose of this paper is to study English tutoring learning systems based on machine learning algorithms. Based on Bootstrap technology, this paper proposes to develop an English tutoring learning system to provide a design idea that conforms to the future multi-platform trend for the design of today's learning software. Using the related technologies of machine learning, an algorithm for judging the reading difficulty based on the English difficulty level is designed. At the same time, in order to enhance the effect of the model, a multi-model fusion algorithm based on multi-feature sets is proposed, and the performance and stability of the model are verified through relevant experiments. The research results will also serve as the basis for user personalized modeling research. © 2022 IEEE.

关键词： Learning algorithms

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Leveraging Tumor Heterogeneity: Heterogeneous Graph Representation Learning for Cancer Survival Prediction in Whole Slide Images 38

Leveraging Tumor Heterogeneity: Heterogeneous Graph Represen...

引用

38th conference on Neural Information Processing systems, NeurIPS 2024

作者： Wu, Junxian Ke, Xinyi Jiang, Xiaoming Wu, Huanwen Kong, Youyong Shao, Lizhi School of Internet Anhui University China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University China Department of Pathology State Key Laboratory of Complex Severe and Rare Disease Molecular Pathology Research Center Peking Union Medical College Hospital Chinese Academy of Medical Sciences Peking Union Medical College Beijing China School of Bioinformatics Chongqing University of Post and Telecommunications China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China

Survival prediction is a significant challenge in cancer management. Tumor microenvironment is a highly sophisticated ecosystem consisting of cancer cells, immune cells, endothelial cells, fibroblasts, nerves and extracellular matrix. The intratumor heterogeneity and the interaction across multiple tissue types profoundly impact the prognosis. However, current methods often neglect the fact that the contribution to prognosis differs with tissue types. In this paper, we propose ProtoSurv, a novel heterogeneous graph model for WSI survival prediction. The learning process of ProtoSurv is not only driven by data but also incorporates pathological domain knowledge, including the awareness of tissue heterogeneity, the emphasis on prior knowledge of prognostic-related tissues, and the depiction of spatial interaction across multiple tissues. We validate ProtoSurv across five different cancer types from TCGA (i.e., BRCA, LGG, LUAD, COAD and PAAD), and demonstrate the superiority of our method over the state-of-the-art methods. Source code is available at https://***/wjx-error/ProtoSurv. © 2024 Neural information processing systems foundation. All rights reserved.

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Model-Based Learning of Information Diffusion in Social Networks 2nd

Model-Based Learning of Information Diffusion in Social Ne...

引用

2nd international conference on Intelligent Computing systems and Applications, ICICSA 2023

作者： Vasra, T. R. Abijeeth Abirami, A.M. Sumitra, S. Department of Information Technology Thiagarajar College of Engineering Tamil Nadu Madurai India Department of Mathematics Indian Institute of Space Science and Technology Kerala Trivandrum India

ISBN: (纸本)9789819754113

In recent years, the ability to predict how information will travel within a network has gained importance, especially in the setting of online social media platforms where the dissemination of information can have a big impact on social and political events. Researchers frequently employ a range of methods, such as network mapping and analysis, data mining, and machine learning algorithms, to study social networks and the connections between them. These methods can be used to locate important nodes and influential nodes within a network and to comprehend the underlying dynamics that govern the dissemination of knowledge. This research work attempts to study the use of three different epidemic models to a medium level twitter network of size 35,000 nodes. This paper discusses the impact of centrality measures, the use of hyperparameters and the results of models in detail. It recommends the suitable social network epidemic model for better information diffusion in the network, which can be adopted by businesses for digital marketing or advertising. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tweets

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SSAT: Scientific Storyboarding Framework Using Artificial Intelligence Techniques

SSAT: Scientific Storyboarding Framework Using Artificial In...

引用

international conference on data Science, Computation and Security, IDSCS 2023

作者： Hariharan, Nitin Deepak, Gerard Department of Computer Science and Engineering Alagappa Chettiar Government College of Engineering and Technology Tamil Nadu Karaikudi India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9789819709748

This paper introduces a scientific storyboarding framework that uses artificial intelligence for learning and inferencing. It works by classifying a dataset of documents using the Bi-LSTM classifier to identify different instances. The results are then used to create metadata, which is further classified using deep belief networks. Next, the user's topic of interest is pre-processed, and an intermediate ontology model is generated. This model helps build knowledge by gathering information from the World Wide Web using recurrent neural networks. The framework then combines the results from two distinct classifiers—one from the dataset and the other from the user's query. These results are analyzed through explicit semantic analysis to select relevant entities. The monkey algorithm is utilized for optimization, resulting in an optimal solution set. To ensure semantic reasoning and entity regulation, Morisita index and explicit semantic analysis is applied. The proposed SSAT model achieves high percentages of precision, recall, accuracy, and F-measure, specifically 95.58, 96.83, 96.205, and 96.2009397%, respectively. Additionally, the model achieves a low value of FDR (False Discovery Rate) at 0.05%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Semantics

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Mitigate Seismic Liquefaction Risk with Nanosilica Grout Injection: engineering Principle and Sustainability Assessment at the Urban Scale

Mitigate Seismic Liquefaction Risk with Nanosilica Grout Inj...

引用

international conference on knowledge Transfer in the Sustainable Rehabilitation and Risk Management of the Built Environment, KNOW-RE-BUILT 2021

作者： Paolella, Luca Salvatore, Erminio Modoni, Giuseppe University of Cassino and Southern Latium Cassino03043 Italy

ISBN: (纸本)9783031434549

Reducing liquefaction risk at the community level embraces a series of subsequent technical and non-technical steps, including communication strategies and political decisions, which are not explicitly discussed here. This study first presents the engineering design principles of nanosilicate grout injections and their application to mitigate the liquefaction risk. In a subsequent step, a cost–benefit analysis is defined and performed to evaluate the economic sustainability at the urban scale of this mitigation technique. Preliminary stages of the benefit–cost analysis consist in evaluating the liquefaction susceptibility of the area of interest, introducing the seismic hazard, and evaluating on an annual basis the resulting risk on the selected systems. The above implies the need to adopt adequate economic loss models and raises the question about the treatment of uncertainties affecting input data and propagating step by step. Bearing this goal in mind, the case study of Terre del Reno (Italy), hit by severe and enormous liquefaction following the 2012 Emilia Earthquake Sequence, is analyzed. After evaluating the liquefaction risk on buildings, the cost–benefit analysis is performed by introducing four nanosilica grout injection scenarios, highlighting the need to balance the technical goal and the cost-effectiveness. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Risk assessment

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Lunatum Prosthetic Replacement: Modeling Based on Volume Rendering of CT Scan Images

Lunatum Prosthetic Replacement: Modeling Based on Volume Ren...

引用

international conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2022

作者： Hamda, Manal Ghali, Btihal El Hilal, Imane Midaoui, Omar El Ngote, Nabil Abdi, Bahia El Megdiche, Kawtar Biomedical Engineering Department Abulcasis International University of Health Sciences Rabat Morocco Department of Data and Knowledge Engineering Information Sciences School Rabat Morocco Rabat Morocco Rabat Morocco Medical Simulation Center Cheikh Zaid Foundation Rabat Morocco

ISBN: (纸本)9789811667749

Additive manufacturing has immersed the medical field, especially in reconstructive surgery, allowing the creation of a 3D model resembling the anatomical structure of interest. Due to Osteonecrosis also referred to as Kienböck’s disease;carpal bones especially the lunatum are concerned the most with those technologies especially since a prosthetic replacement is an obligation when it comes to advanced stages of this disease. In this article, we propose a method based on direct 3D reconstruction based on volume rendering directly on patients’ medical images (CT scans) to preserve the anatomical shape. For that purpose, we utilized 3D slicer software to create a 3D model based on different cuts of CT scan images. The resulting model was satisfactory, as it was similar to the lunate bone structure preserving all its anatomical characteristics and dimensions. The proposed approach helps in creating a prosthetic replacement with the exact anatomical shape and structure of the bone of interest respecting the dimensions, curves, and facets. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Prosthetics

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Process Model for Digital Transformation of University knowledge Transfer 13

Process Model for Digital Transformation of University Knowl...

引用

13th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management (IC3K) / 13th international conference on knowledge Management and Information systems (KMIS)

作者： Doering, Claudia Reiche, Finn Timinger, Holger Univ Appl Sci Inst Data & Proc Sci Landshut Germany

ISBN: (纸本)9789897585333

The digital transformation is still a volatile concept with different understandings between researchers and practitioners. Nevertheless, digital technologies and concepts are finding their way into all areas of life, be it private or professional life. Even universities are not spared from digital transformation. They need to incorporate innovations not only within their curricula, but also in their inner structures and administration to ensure up to date research and transfer. Therefore, a structured model for the digital transformation of transfer in universities was created. The main purpose of this paper is to provide practical support and break down barriers in the digital transformation of knowledge transfer in and out of universities.

关键词： Digitalization Digital Transformation knowledge Management Transfer Higher Education Institution Universities Modeling for Digitalization

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ACCELERATED AGING OF ADHESIVELY BONDED COMPOSITE JOINTS FOR USE IN MATERIAL SCREENING AND SELECTION DURING DEVELOPMENT: A REVIEW AND PROPOSED MODEL

ACCELERATED AGING OF ADHESIVELY BONDED COMPOSITE JOINTS FOR ...

引用

SAMPE 2024 conference and Exhibition

作者： Flanigan, Marie D. College of Engineering Science of Florida Institute of Technology MelbourneFL United States

ISBN: (纸本)9781934551455

Adhesively bonded structures are used in many high-tech industries such as aerospace, automotive, and defense. The application of these joints using composite adherends has increased in recent years because of improved strength-to-weight ratio, design flexibility, and lower concern for corrosion. Quality acceptance of these structures largely depends on reliability and durability which includes influence of environmental service conditions on long-term behavior. Characterization of long-term behavior presents a difficult challenge to those involved in material selection and design due to the long service life of the joint compared to the development time available and complexity of the degradation mechanisms. Therefore, different testing approaches are needed to address aging of bonded systems. First, an introduction to design, testing, and analysis of adhesively bonded composite structures is reviewed, along with an in-depth assessment of degradation mechanisms related to bonded composite joints. Next, current research, methodologies, and knowledge gaps on accelerated aging of structurally bonded composites involving the Arrhenius Reaction Rate theory are discussed. Finally, an empirical model and case study is proposed as a hypothetical data set example for using Arrhenius methodology, statistically designed experimental techniques, and verification methods. This hypothetical model is intended to provide guidance for predicting and verifying long-term behavior of adhesively bonded composite aerospace structural joints in an ambient storage environment to aid in material selection during development. © 2024 Soc. for the Advancement of Material and Process engineering. All rights reserved.

关键词： Reaction rates

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A Model Inference Attack Based on Random Sampling in DLaaS 1

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24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wu, Feng Sun, Shouyue Yang, Jiaxun Wu, Liwen Cui, Lei Qu, Youyang Yao, Shaowen Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Engineering Research Center of Cyberspace Yunnan University Kunming China School of Information Science and Engineering Yunnan University Kunming China Data61 Commonwealth Scientific and Industrial Research Organization BurwoodVIC3166 Australia

ISBN: (数字)9789819615513

ISBN: (纸本)9789819615506

Deep learning as a service (DLaaS) has become an effective business solution in numerous domains. Nevertheless, its service form has been proven vulnerable to model inference by previous research. Existing work on model inference attack relies on strong assumptions, such as the attacker knows the sample variance and model structure, which may not thoroughly reflect the attack’s potential threats. In this paper, we propose MC-infer, a zero-knowledge, real-data-free, and black-box model inference method inspired by Monte-Carlo sampling. In particular, MC-infer feeds random noises obtained from different distributions to the target model, and sniffs the corresponding target distributions according to its feedback. Then many samples are taken in these distributions to obtain enough robust noises. Finally, the distribution represented by the target model is fitted through these noises to perform model inference. Our extensive evaluations demonstrate that MC-infer can effectively infer the target model with less information, and general noise perturbation on the model’s outputs cannot defend against MC-infer. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Intelligent systems

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