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Structural Health Monitoring Data Analysis Using Deep learning Techniques 24

Structural Health Monitoring Data Analysis Using Deep Learni...

引用

8th international conference on Electronic Information Technology and Computer engineering, EITCE 2024

作者： Yang, Linfei Huang, Enzo Faculty of Science and Technology University of Macao China Faculty of Business Administration University of Macao China

ISBN: (纸本)9798400710094

With the rapid development of deep learning technology, its application in the field of Structural Health Monitoring (SHM) represents a significant leap from traditional detection methods. This paper aims to explore how deep learning algorithms enhance the assessment of civil infrastructure integrity by analyzing large datasets from sensors. We first introduce the background of SHM, highlighting the shortcomings of traditional methods in terms of accuracy and efficiency. Next, we employ deep learning models for data analysis, investigating their applications in structural damage detection and prediction. Through a comprehensive analysis of existing literature, we find that deep learning not only overcomes the limitations of manual inspections but also significantly improves the overall performance of SHM by enhancing real-time monitoring capabilities. Furthermore, this paper discusses hybrid systems that combine data-driven models with physical understanding, as well as the distributed intelligence brought about by edge computing. These factors collectively provide more reliable and flexible solutions for future infrastructure management. Our key findings indicate that the application of deep learning can significantly improve the accuracy and efficiency of SHM, enabling a quicker response to the needs of dynamic communities. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Construction and Evaluation of Experimental and Practical teaching Cases Based on the Knowledge Graph 14

Construction and Evaluation of Experimental and Practical Te...

引用

14th international conference on Educational and Information Technology, ICEIT 2025

作者： Wang, Yaping Xu, Jiangxu Fan, Yuqi Fu, Xiangfu Zhang, Anshan Jiang, Jingang School of Mechanical and Power Engineering Harbin University of Science and Technology Harbin China

ISBN: (纸本)9798331540883

In the face of intelligent manufacturing, new quality productivity, and AI empowerment, the digital construction of teaching resources in the experimental teaching demonstration centers is a necessity of the times and an inevitable trend in the cultivation of new engineering talents. In the context of the education digital transformation from the teaching centered to the learning centered teaching model, it is particularly important to provide students with the personalized, the hierarchical, and the modular knowledge system architecture. This article is based on the intelligent manufacturing innovation platform and uses the knowledge graph to construct typical cases of the digital experimental and the practical teaching. It deeply integrates knowledge objectives, ability objectives, and ideological and political objectives, builds a practical knowledge system from the student perspective, the cultivates their comprehensive ability of the independent learning and the innovative practice, and establishes a multi-scale entropy based the experimental and practical case teaching evaluation method to explore the reform of experimental and the practical teaching based on the knowledge graph, truly realizing the talent cultivation goal and the education mode of the new engineering concept of OBE. © 2025 IEEE.

关键词： Students

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Analyzing the Impact of AI Tools on Student Study Habits and Academic Performance 15

Analyzing the Impact of AI Tools on Student Study Habits and...

引用

15th IEEE Annual computing and Communication Workshop and conference, CCWC 2025

作者： Ward, Ben Bhati, Deepshikha Neha, Fnu Guercio, Angela Kent State University Dept. of Computer Science KentOH United States

ISBN: (纸本)9798331507695

This study explores the effectiveness of AI tools in enhancing student learning, specifically in improving study habits, time management, and feedback mechanisms. The research focuses on how AI tools can support personalized learning, adaptive test adjustments, and provide real-time classroom analysis. Student feedback revealed strong support for these features, and the study found a significant reduction in study hours alongside an increase in GPA, suggesting positive academic outcomes. Despite these benefits, challenges such as over-reliance on AI and difficulties in integrating AI with traditional teaching methods were also identified, emphasizing the need for AI tools to complement conventional educational strategies rather than replace them. Data were collected through a survey with a Likert scale and follow-up interviews, providing both quantitative and qualitative insights. The analysis involved descriptive statistics to summarize demographic data, AI usage patterns, and perceived effectiveness, as well as inferential statistics (T-tests, ANOVA) to examine the impact of demographic factors on AI adoption. Regression analysis identified predictors of AI adoption, and qualitative responses were thematically analyzed to understand students' perspectives on the future of AI in education. This mixed-methods approach provided a comprehensive view of AI's role in education and highlighted the importance of privacy, transparency, and continuous refinement of AI features to maximize their educational benefits. © 2025 IEEE.

关键词： Adaptive learning paths AI in education Personalized learning Real-time feedback Study habits

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Enhancing Student Grade Prediction Using Hybrid Stacking and Blending Regression Models 5

Enhancing Student Grade Prediction Using Hybrid Stacking and...

引用

5th international conference on Emerging Systems and Intelligent computing, ESIC 2025

作者： Bhoi, Manjulata Padhy, Neelamadhab Panigrahi, Rasmita Giet University School of Engineering and Technology Department of Computer Science and Engineering Odisha Gunupur765022 India

ISBN: (纸本)9798331522100

This research explored the application of hybrid machine learning models for forecasting student grades by combining stacking and blending regression techniques with a combination of machine learning techniques, including linear, lasso, ridge, and support vector regression, to predict student grades. The effectiveness of the model in capturing performance-related variables was demonstrated by its R-squared value of 0.9999. This study highlights the significance of the RMSE in assessing prediction accuracy. With its low MSE and RMSE, coupled with an outstanding R-squared value, the model enables educators to recognize at-risk students and develop targeted interventions to enhance educational outcomes. © 2025 IEEE.

关键词： Students

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Intelligent Timetable Based on WeChat Mini Program 24

Intelligent Timetable Based on WeChat Mini Program

引用

8th international conference on Electronic Information Technology and Computer engineering, EITCE 2024

作者： Jing, Ru Gao, Xiaolong Bian, Zhancai Wang, Lihan College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China

ISBN: (纸本)9798400710094

This study aims to help users better plan and manage daily learning and teaching activities by providing a convenient and efficient schedule management tool, and optimize the educational administration process with a variety of technical means to improve students’ learning experience. The project automatically captures the latest schedule information from the school official website, and uses multi-process technology to speed up the data processing speed, so as to ensure the timely update and efficient management of information. The project uses OpenID authentication for secure user authentication, combined with the Nginx server and the API interface developed by PHP, to provide a stable and reliable system access environment. All course data are stored in the MySQL database, and multiple restrictions are implemented, and different user rights are set, including constraints between tables and tables to maximize the risk of SQL injection. With small programs, users can subscribe to the long-term messaging service. After the subscription service is opened, the smart class schedule will remind the user of the course arrangement before the class, and the user can also set the reminder time point. This function greatly reduces the time required for users to query the class schedule, realizes the convenient experience of opening and seeing immediately, and helps users to better plan their daily learning and teaching activities. © 2024 Copyright held by the owner/author(s).

关键词： Authentication

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Using Machine learning to Enhance Personalization in E-learning

Using Machine Learning to Enhance Personalization in E-Learn...

引用

2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Palimkar, Sneha S. Kunnathully, Kannan Thota, Srinivasarao Joshi, Meeta Imran Akkalkot, Afsha Al Said, Nidal Coep Technological University Computer Engineering and Technology Maharashtra Pune India Veltech School of Law India Capital One Software Engineering VA United States Marwadi University Faculty of Management Studies India Pune Technological University Computer Science Engineering College of Engineering Maharashtra Pune India Ajman University College of Mass Communication Ajman United Arab Emirates

ISBN: (纸本)9798331508685

Numerous diverse learning materials can be found on e-learning sites. Students in today's e-learning platforms invest a lot of time and energy in locating pertinent learning materials. The student's actual requirements must be taken into account based on a variety of characteristics, including choices, expertise, and learning style. Education must be pertinent to the necessary concept's environment. This study aims to develop an efficient approach for detecting e-learning style and then customizing the e-learning contents to match that style, using machine learning (ML) algorithms to enhance personalization in e-learning. The blended ensemble method combined with the XGBoost meta-learning approach produced the most prominent results for enhancing e-learning style, with an accuracy of 97.6%. Further, the textual material of the e-files is altered using various natural language processing (NLP) approaches. The spaCy NLP-oriented labeled entity identification (LEI) algorithm achieves a 94.2% F1 value and a 0.92 precise match ratio while color-coded textual production of 10 e-files with 790 different phrases. These alterations are intended to suit students' tastes, resulting in an additional personalized and interactive teaching encounter. © 2025 IEEE.

关键词： and Natural language processing Artificial intelligence E-learning Machine learning Students

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Transfer learning based Analysis of Chest X-rays for Accurate Lung Disease Detection and Interpretation 3

Transfer Learning based Analysis of Chest X-rays for Accurat...

引用

3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Perla, Someswari Veledendi, Sudhakar Jaiswal, Pooja Muppidi, Satish Mandala, Jyothi Maram, Balajee Gmr Institute of Technology Department of Cse - Ai&ml Andhra Pradesh Rajam India Sr University School of Computer Science and Artificial Intelligence Warangal India Galgotias University India Gmr Institute of Technology Departnent of Computer Science and Engineering Rajam India University Department of Computer Science and Engineering Banglore India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India

ISBN: (纸本)9798331515911

This is a research paper based on a transfer learning approach with a primary aim at the analysis of chest Xrays for accurate detection and interpretation of lung diseases. The proposed method relies heavily on the use of pretrained deep learning models to enhance diagnostic accuracy and reduce the time and computational resources taken during training. Applying transfer learning to a large chest X-ray dataset, the model successfully detects key patterns associated with common lung diseases, such as pneumonia and tuberculosis. The manuscript encompasses data preprocessing, model finetuning, and performance evaluation and demonstrates huge improvements over the traditional methods both in terms of accuracy and interpretability. It has been experimentally proven that the model is competent enough to provide localization of disease areas, as it can be visualized through heatmaps obtained from predictions, which might further help the radiologists perform their diagnosis tasks. This work advocates for medical imaging automation for the early and efficient detection of lung disease. © 2025 IEEE.

关键词： Lung cancer

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Automated Classification and Segmentation of Brain Tumor Images using Backboned ResUNet Framework 6

Automated Classification and Segmentation of Brain Tumor Ima...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Prasanalakshmi, B. Anitha, Cuddapah Balamanigandan, R. Mahaveerakannan, R. Gade, N.V. Ratnakishor Adjunct Faculty Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Computer Science and Engineering School of Computing Mohan Babu University Andhra Pradesh Tirupati India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9798331522667

One of the most challenging issues in computer imaging is the automated segmentation of brain tumors using Magnetic Resonance Images (MRI). Several approaches are explored using Deep Neural Networks in image segmentation since they perform well in automated tumor segmentation using tumor scans. Due to the complexity, training deeper neural networks usually takes a long time and many processing resources. An automated approach for classifying and segmenting images of Brain tumor MRI is proposed. As a hybrid transfer learning approach, modified ResNet50 identifies the class of MRI images as Benign or malignant. After identifying the Benign images, the tumor’s location is segmented using a hybrid network involving ResUNet with the Xception network as Backbone. This combination approach is chosen after its comparative analysis with other backbones. Various combination approaches are carried out, and performance metrics like Jaccard, Dice, and Tversky coefficient are done to prove the ResUNet backboned with Xception plays a better role concerning accuracy and speed. The proposed approach was tested on the dataset Cancer Imaging Archive to validate its efficiency. The proposed model has an average dice similarity coefficient score of 0.94, outperforming the conventional transfer learning training method with all the layers. © 2025 IEEE.

关键词： Magnetic resonance imaging

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JaxDecompiler: Redefining Gradient-Informed Software Design 7th

JaxDecompiler: Redefining Gradient-Informed Software Design

引用

7th international conference on Optimization and learning

作者： Pochelu, Pierrick Univ Luxembourg FSTM DCS Esch Sur Alzette Luxembourg

ISBN: (纸本)9783031779404;9783031779411

Among numerical libraries capable of computing gradient descent optimization, JAX stands out by offering more features, accelerated by an intermediate representation known as Jaxpr language. However, editing the Jaxpr code is not directly possible. This article introduces JaxDecompiler, a tool that transforms any JAX function into an editable Python code, especially useful for editing the JAX function generated by the gradient function. JaxDecompiler simplifies the processes of reverse engineering, understanding, customizing, and interoperability of software developed by JAX. We highlight its capabilities, emphasize its practical applications especially in deep learning and more generally gradient-informed software, and demonstrate that the decompiled code speed performance is similar to the original.

关键词： Gradient Descent Software Design Decompilation Deep learning

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To Serve E-learning: A Generative Dialogue Framework Based on Fine-Tuned Large Language Model 1

To Serve E-Learning: A Generative Dialogue Framework Based o...

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1st international conference on Intelligent Manufacturing and Cloud computing, ICIMCC 2024

作者： Zhou, Fang Liu, Yanhong Xiao, Jing College of Information Science and Technology Shihezi University Shihezi China College of Computer and Information Engineering Xinjiang Agricultural University Urumqi China

ISBN: (纸本)9781643685779

E-learning has always been the focus of modern education. Since 2023, the rapid development of open-source large language models may bring revolutionary benefits to E-learning. Taking the classroom teaching of the Practical Tutorial for University Computer Basics Spoc as an example, this paper designs a chatbot "AI-Assisted Teacher" based on a fine-tuned LLaMA3 and applies it to E-learning environments. With the help of machine learning and knowledge graphs, a course-specific dataset was created. The large language model overcomes the shortcomings of easy hallucination and poor decision-making ability to a certain extent, enabling the "AI-Assisted Teacher" to engage in a more friendly and professional dialogue with students in E-learning environments. Experiments show that the fine-tuned LLaMA3 has significantly improved its understanding of the comprehensive education dataset compared to the basic large language model, with the best Bleu-4, Rouge-1, Rouge-2, and Rouge-L scores being 25.44, 42.42, 21.77, and 29.47, respectively. During the manual testing phase, the chatbot "AI-Assisted Teacher" can teach course knowledge, and more importantly, the dialogue mode is closer to that of a real-life teacher. This paper combines machine learning, knowledge graphs, and large language models to apply E-learning for the first time, providing ideas for colleges and universities to explore E-learning environments. © 2025 The Authors.

关键词： Adversarial machine learning

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