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Data mining-based evaluation of English teachers' teaching ability 2

Data mining-based evaluation of English teachers' teaching a...

引用

2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Tang, Yingling Foreign Language School Chengdu Neusoft University Sichuan Chengdu611844 China

ISBN: (纸本)9781510689053

This study proposes a data mining-based approach to evaluate English teachers' teaching abilities, addressing the limitations of traditional evaluation methods. By integrating techniques such as data preprocessing, rough set theory, and association rule mining, the approach improves the accuracy and reliability of assessments. The method uses a comprehensive set of evaluation indicators, including academic qualifications, classroom performance, research abilities, and after-class guidance. Through data discretization, attribute reduction, and weight calculation, the method produces a more accurate and representative teaching ability score. Experimental validation conducted in a local school shows that the proposed method outperforms traditional methods like weighted average and fuzzy mathematics in terms of evaluation accuracy. The results demonstrate that the data mining approach better reflects the true teaching abilities of educators, offering a more reliable and scientifically grounded evaluation. The study highlights the importance of incorporating diverse indicators and the potential for further improvement through advanced machine learning algorithms. © 2025 SPIE.

关键词： Principal component analysis

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Machine learning in Higher Education using RF and SVM for Pedagogical Enhancement 3

Machine Learning in Higher Education using RF and SVM for Pe...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Kunwar, Fateh Bahadur Srivastava, Satyajee Dhivya, R. Gulati, Monika Anand, Taruna Raj, D. Antony Arul Geetanjali Institute of Technical Studies Department of Computer Science and Engineering Udaipur India School of Engineering and Technology Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Faridabad India M.Kumarasamy College of Engineering Department of Information Technology Karur India Ambala India University Department of Humanities and Social Sciences Dehradun India Psg College of Arts & Science Department of Software Systems Coimbatore India

ISBN: (纸本)9798331508456

AI's revolutionary potential in higher education is examined in this proposal, including how it could revolutionize teaching, learning, administration, and research. Adaptive learning platforms, intelligent tutoring systems, and virtual learning assistants may change how students learn by improving engagement, providing quick feedback, and enabling tailored learning. The suggested process includes preprocessing, feature extraction, and model training. Preprocessing involves de-anonymizing, tokenizing, and removing punctuation, spaces, and numbers before analysis. The linear transformation method PCA extracts features. Training the systems uses the RF -SVM approach, which combines the advantages of RF and SVM. The suggested RF -SVM model beats two RF and SVM models with an average accuracy of 90.39 *** improves resource use, strategic decision-making, and research capacity in university management. The RF-SVM model's success in academia highlights the practicality of AI-driven solutions. © 2025 IEEE.

关键词： teaching

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A Federated learning Network Intrusion Detection System for Multiple Imbalances

A Federated Learning Network Intrusion Detection System for ...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zeng, Yifan Zheng Li, Jian School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Jinan China

ISBN: (纸本)9798350368741

Centralized training of deep learning-based network intrusion detection systems (DLNIDS) raises privacy concerns and incurs huge overhead. Federated learning (FL), while preserving privacy, confronts challenges including size imbalance, heterogeneous and global imbalance. To address these issues, this paper proposes a Self-Balancing Federated learning Network Intrusion Detection System (SBFedNIDS) for accurate and efficient network intrusion detection while preserving privacy and tackling negative impact caused by multiple imbalances resulting from FL. SBFedNIDS can efficiently mitigate model bias cause by multiple imbalances and addresses imbalances at data-level via federated cost sensitive learning (FCSL) and client synthetic minority over-sampling technique (CSMOTE), without requiring additional training resources, unlike generative models. Extensive experiments on a benchmark dataset show that SBFedNIDS outperforms baselines in multiple unbalanced scenarios and even surpasses centralized training in several settings. © 2025 IEEE.

关键词： Data Imbalance Deep learning Federated learning Network Intrusion Detection

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Application of the S-PPED-C learning Model for Developing Embedded teaching Innovations 27th

Application of the S-PPED-C Learning Model for Developing Em...

引用

27th international conference on Interactive Collaborative learning, ICL 2024

作者： Inchan, Surasak Akatimagool, Somsak Department of Teacher Training in Electrical Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (纸本)9783031856488

Nowadays, modern industries and smart technologies are focused on the application of intelligent technology. However, most employees still lack high ability in embedded systems. To solve this problem, the engineering education system must upskill and adapt the basic skills of embedded technology as much as possible. This research aims to apply the S-PPED-C learning model to develop embedded teaching innovations. The S-PPED-C learning model based on blended learning management was designed with 6 steps: surveying, preparing, practicing, evaluating, discussing, and concluding. The innovative learning package created includes a teacher’ manual, learning activity plan, laboratory-based learning media for embedded systems, and achievement tests. The research tool was implemented with a sample of 16 undergraduate students in the Department of Information and Communication engineering, the Faculty of engineering and Industrial Technology at Phetchaburi Rajabhat University, Thailand. The results of the research found that;1) the developed S-PPED-C learning model was effective with the E1/E2 efficiency value of 83.13/83.33 according to the standard criteria of 80/80, 2) the evaluated results of the learning achievement of the students were 81.26% according to the standard criteria, and 3) the comparison of learning achievement of student after studying was significantly higher than before studying at the.05 level. It can be seen that the developed S-PPED-C learning model can promote learners with learning skills in the 21st century and is consistent with professional standards in the Industry 4.0 era. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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An approach for early prediction of multi-disease using ML techniques 2nd

An approach for early prediction of multi-disease using ML t...

引用

2nd international conference on Next-Generation computing and Information Systems, ICNGCIS 2023

作者： Malagar, Vani Sharma, Mekhla Upadhyay, Navin Mani Model Institute of Engineering and Technology J&K Jammu India

ISBN: (纸本)9781032738659

Leveraging the explosion of medical data including patient histories, electronic health records & medical imaging, the integration of machine learning techniques with healthcare for multi-disease prediction has experienced tremendous growth. Several algorithms have already been proposed by many researchers but they cannot fit for all the solutions at once. So, this research paper uses Machine learning (ML) techniques to investigate the complexity involved in multi-disease prognosis in the context of convergence. It explores three well-knows ML- algorithms (Decision Tree, Random Forest classifier and Gradient Boosting) to investigate and compare the best way to identify and diagnose the prediction problems. Further a detailed comparison with existing algorithms shows a better result from 70%–77%. The accuracy of used algorithm has shown 78.57% for Decision Tree, 77.92% for Random classifier and for Gradient Boosting 95.38%. Similarly, the F1-score is also improved up to 3%. The paper also redefines several machine learning algorithms used by several researchers to solve the prediction problem, by emphasizing the importance of gradient boosting as a successful strategy. This work also validates the used models based on a statistical analysis "ANOVA and Chi-square". © 2025 The Author(s).

关键词： Decision trees

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Design of college English oral training system based on Collaborative Filtering Algorithm 2

Design of college English oral training system based on Coll...

引用

2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Hou, Pingying Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (纸本)9781510689053

Existing English oral training systems often rely on static rules and lack the ability to dynamically adjust recommended content. They also fail to effectively promote interaction and cooperative learning among students, resulting in a mismatch between practice materials and actual needs and a lack of social interaction. To this end, this paper designs a college English oral training system based on collaborative filtering algorithm. The system collects students' practice records and grades and other learning data to build a user feature model, and then uses collaborative filtering algorithm to analyze the learning preferences of similar users and dynamically recommend personalized oral practice content. The system also recommends learning partners based on user similarity to enhance interactivity. Experimental results indicate that the recommendation accuracy of the system reaches 92%, and the average oral test score in group cooperation scenes reaches 96 points. This shows that the method proposed in this paper effectively improves the students' oral training effect and promotes cooperation and communication among learners. © 2025 SPIE.

关键词： Collaborative filtering

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Socially Sustainable HRI for Underrepresented Communities: Case Study of Language learning of Arab Children in Israel 20

Socially Sustainable HRI for Underrepresented Communities: C...

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20th Annual ACM/IEEE international conference on Human-Robot Interaction, HRI 2025

作者： Gonen, Einat Abu-Mukh, Amna Gordon, Goren Tel-Aviv University Department of Hebrew language and Semitic languages Tel-Aviv Israel Tel-Aviv University Curiosity Lab Department of Industrial Engineering Tel-Aviv Israel Luddy School of Informatics Computing and Engineering Indiana University Department of Informatics BloomingtonIN United States

ISBN: (纸本)9798350378931

Social robots have predominantly been deployed in resource-rich environments and managed by technical research teams. We propose a socially sustainable framework for deploying social robots in underrepresented, under-resourced communities. This approach emphasizes population-specific social impact, affordable robotic platforms, and 'programming' and operation by local community members. We present a holistic and sociallyecological case study of a unique population: Arabic elementary school children in Israel who face specific linguistic challenges. learning Hebrew as a second language is crucial for their future, but teaching resources are limited. We developed a unique curriculum designed to teach bilingual morphological awareness in a fun and engaging way, implementing it through social robotic platforms. These platforms were selected based on social sustainability criteria, including affordability, lack of required programming skills by educators, and ease of deployment in schools. In two long-term, in-the-wild studies conducted at two different elementary schools (N=135, N=87), we utilized first Patricc and then Valera, both open-source, 3D-printed robots. These robots were 'programmed' by a non-technical educator and operated by local teachers. After six activities conducted over several weeks, our results show significant positive learning gains in both Arabic and Hebrew morphological awareness. We discuss the implications of our socially sustainable framework for using social robots in underrepresented and under-resourced communities. © 2025 IEEE.

关键词： teaching

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Machine learning Approaches for Insect Species Recognition using Pyramid Vision Transformer 3

Machine Learning Approaches for Insect Species Recognition u...

引用

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

作者： Soundarya, M. Iswarya, G. Shree, P. Pooja Suganthi, Swarna Velammal College of Engineering and Technology Department of It Madurai India

ISBN: (纸本)9798331515911

Insect species recognition systems have significantly advanced with recent machine learning techniques, but many existing models still face challenges such as limited scalability, lower accuracy in diverse environments, and poor generalization across species variations. To address these limitations, we propose an insect species recognition system utilizing the Pyramid Vision Transformer (PVT) algorithm, which excels in handling hierarchical and multi-scale features. By utilizing a hybrid dataset that was produced by merging the Insect Village Synthetic Dataset and the Agricultural Pest Dataset, the proposed approach exceeds traditional models by a wide margin, achieving a recognition accuracy of 99.16%. The PVT-based system also demonstrates robustness in recognizing complex and diverse insect species, making it an effective solution for entomological studies. © 2025 IEEE.

关键词： Invertebrates

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Predictive Modeling for Assessing Collision Risk of Space Debris with Operational Satellites: Enhancing Safety and Mission Planning 3

Predictive Modeling for Assessing Collision Risk of Space De...

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

作者： Hewin, A. Sriram, R. Jegadeesan, S. Velammal College of Engineering and Technology Department of Information Technology Tamilnadu Madurai India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

The operational integrity of satellites is seriously threatened by space debris, as potential collisions might result in expensive damage and mission failures. The goal of this project is to create a predictive model that will evaluate the likelihood of space debris colliding with satellites in operation. The model will provide precise risk evaluations by using orbital characteristics and historical debris data, improving satellite safety and supporting mission planning. To forecast collision probability, the method includes gathering data, preprocessing it, creating features, and using machine learning algorithms. A detailed grasp of the mechanics of space debris and how it interacts with operational satellites is necessary for the development of. Large datasets will be processed and analyzed using advanced data analytics and machine learning techniques to find patterns and trends that affect the likelihood of collisions. © 2025 IEEE.

关键词： Space debris

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Inference method for power large-scale model operation based on distributed computing resource scheduling

Inference method for power large-scale model operation based...

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2024 international conference on Physics, Photonics, and Optical engineering, ICPPOE 2024

作者： Huang, Xiaoguang Qin, Yu Liu, Yaqing Cui, Yingbao Guo, Ting Gu, Qingtong State Grid Information and Communication Industry Group Co. Ltd Beijing102211 China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

Electric power computing resources are distributed in the respective regions of provincial data centers and transmission and transformation equipment, and the scheduling efficiency of computing resources is limited by conditions such as long distribution locations. Composite data model inference has been widely used in power equipment maintenance analysis, and large-scale model inference requires the use of distributed computing resources. In order to improve the scheduling and fusion efficiency of distributed computing power resources, this paper adopts a lightweight language model to infer and calculate the scheduling parameters of computing power resources in different regions. At the same time, in order to make more rational use of computing power resources, the intelligent models and algorithms used for power equipment maintenance are optimized. Configure the adaptation parameters of the model according to different application scenarios, and adjust the fusion method of input data for the fault diagnosis model and the classification strategy for the fault prediction model separately. Using a fine-tuning language model to map the predicted results to the semantics of power business, and then enhancing the rationality and interoperability of the answer results based on historical knowledge memory, ultimately pushing the reasoning results of the reasons and the classification results of the equipment defect diagnosis classifier to the equipment diagnosis designer. © 2025 SPIE.

关键词： Data modeling Distributed computing Education and training Video Machine learning Semantics Video coding Instrument modeling Power grids Target detection

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