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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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The Impact of Using Deep learning Technology in Developing Smart Programs for Teaching Arabic to Non-Native Speakers 1st

The Impact of Using Deep Learning Technology in Developing S...

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1st international conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Hussein, Alaa Abdulkhaleq Farhan, Neamah Dahash University of Baghdad / College of Islamic Science Baghdad Iraq

ISBN: (纸本)9783031823763

This study aimed to explore the impact of utilizing deep learning technology in developing intelligent software for teaching Arabic to non-native speakers. An experimental methodology was employed, involving two groups (experimental and control) with both pre-tests and post-tests. The sample consisted of 30 Arabic learners who are non-native speakers at the Arabic Language Teaching Institute in collaboration with the German Board, divided equally between the two groups. The control group received traditional instruction, while the experimental group was taught using software based on deep learning technology. The instrument of the study was a test that assessed the four Arabic language skills (listening, speaking, reading, and writing). Results indicated statistically significant differences between the mean scores of the two groups in the post-test, favouring the experimental group across all language skills. This suggests the effectiveness of using deep learning technology in developing intelligent software for teaching Arabic to non-native speakers. The study recommends the adoption of this technology in Arabic language education programs and suggests further research to refine these programs and identify their strengths and weaknesses. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Evaluation of the Compressive strength of Fly Ash- Based Geopolymer Concrete Using machine learning 1st

Evaluation of the Compressive Strength of Fly Ash- Based Geo...

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1st international conference on Net-Zero Built Environment: Innovations in Materials, structures, and Management Practices, NTZR 2024

作者： Bypour, Maryam Yekrangnia, Mohammad Kioumarsi, Mahdi Department of Civil Engineering Semnan University Semnan Iran Department of Civil Engineering Shahid Rajaee Teacher Training University Tehran Iran Department of Built Environment OsloMet – Oslo Metropolitan University Oslo Norway

ISBN: (纸本)9783031696251

The advancement of geopolymer concrete technology presents a sustainable solution to address the challenges associated with the significant carbon footprint of the construction industry. This research uses machine learning methods to predict the compressive strength of fly ash-based geopolymer concrete using a wide range of experimental results. Furthermore, the effect of different features, including the components of fly ash-based geopolymer concrete, alkaline activator, and other additives, was investigated utilizing Shapley values technique. The results show that both XGBoost and linear regression models can predict the compressive strength of the studied dataset with acceptable accuracy. In XGBoost approach, R2 values for the train and test data were obtained 0.98 and 0.86, respectively, which leads to more accurate results than linear regression method. Additionally, based on the results, MgO is regarded as the most influential factor in compressive strength compared to other investigated features. Furthermore, the concentration of the alkaline activator, NaOH, positively impacts the target value. © The Author(s) 2025.

关键词： Geopolymer concrete

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Utilizing machine learning Techniques for Analyzing Historical Texts: Extracting Information and Patterns from Historical Documents 1st

Utilizing Machine Learning Techniques for Analyzing Historic...

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1st international conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Qasim, Farah Mustafa Ali, Haneen Abdullah Saleh Salman, Maysoon Word Al–Zarkani, Khalil Hassan University of Baghdad College of Islamic Sciences Baghdad Iran

ISBN: (纸本)9783031823763

This research is essential for filling the critical gap in contextualized historical text analysis: it requires that we extract insights from large scale, landed structured historical documents without losing or reducing the contexts in which phrases were used. Traditional methods often fall short in exposing the intricate socio-cultural dynamics nested in these texts. This study presents an end-to-end machine learning framework that combines NLP, sentiment analysis and deep learning techniques to automatically identify patterns, sentiments, and semantic nuances in ancient texts. The approach includes prepossessing significant historical data, feature engineering, and model building. Experimental data validate that, relative to traditional methods building model outperforms by some margin which is significant as shown in the key findings. To illustrate, the model could identify emotional undertones in the sentiment analysis had an accuracy of 92%, while spice fell short at just 76%. Further, this ML engine achieved an 88% precision in latent themes contractions compared to conventional thematic analysis methods with an average of 67%. Moreover, machine learning could transform the experience of historical research by allowing historians to discover hitherto unseen patterns and narratives. It also increases the speed and effectiveness of historical analysis, adding a component to studying socio-political events. The findings of the study demonstrate considerable potential for AI-based instruments in the humanities, setting the stage for further interdisciplinary work uniting computational techniques and historical research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Personalized Healthcare Recommendations with Q-learning Reinforcement learning 1st

Personalized Healthcare Recommendations with Q-Learning Rein...

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1st international conference on machine learning algorithms, icmla 2024

作者： Tamrakar, Poi Pathak, Ganesh R. Lal, Mily Goel, Akanksha Bhende, Manisha Dr. D. Y. Patil School of Science and Technology Dr. D. Y. Patil Vidyapeeth Maharashtra Pimpri Pune India MIT Art Design and Technology University Maharashtra Pune India

ISBN: (纸本)9783031758607

The transforming potential of Q-learning in customized medical recommendations is examined in this study. Its performance in comparison to conventional methods, strategic data utilization, and flexibility in a variety of scenarios all show great promise. However, a significant gap in the literature emphasizes how important it is to take ethics into account when using patient data. The findings show that Q-learning improves patient outcomes, but its ethical implications are still mostly unknown. This study offers ethical guidelines for future research that address the identified gap. In short, even though Q-learning has many advantages, ethical issues must be bridged in order to promote responsible integration into healthcare, striking a balance between technical advancements as well as moral principles to enhance patient outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adaptability Ethical Considerations Personalized Healthcare Q-learning

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machine learning-Aided Prediction of Seismic Response of RC Bridge Piers Exposed to Chloride-Induced Corrosion 1st

Machine Learning-Aided Prediction of Seismic Response of RC ...

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1st international conference on Net-Zero Built Environment: Innovations in Materials, structures, and Management Practices, NTZR 2024

作者： Poorahad Anzabi, Pooria Shiravand, Mahmoud R. Mahboubi, Shima Faculty of Civil Water and Environmental Engineering Shahid Beheshti University Tehran Iran

ISBN: (纸本)9783031696251

Different environmental issues such as carbonation and corrosion due to chloride threaten aging reinforced concrete (RC) bridges that are in service in areas highly prone to corrosion and earthquakes. Significant experimental and numerical efforts have been put into scrutinizing the effect of corrosion on nonlinear behavior of structural elements. With the rapid development of artificial intelligence, useful methods are now provided to allow for the assessment of such bridges without the drawbacks and limitations of the experimental and numerical methods. In this paper, four machine learning (ML) algorithms are employed;linear regression (LR), decision tree (DT), random forest (RF), and XGBoost for data fitting of the models, and Bayesian search is used for optimization of hyperparameters. Numerical models of RC piers with stochastic parameters defining geometry, loading, and materials are built, and the degradation due to corrosion is applied with a randomly determined level of corrosion. Then, the corroded models are nonlinearly analyzed with random ground motions scaled to design-based and maximum credible earthquake spectra, and maximum drift ratios are stored. Using the created database, different ML models are compared to find the most accurate one. R-squared, mean absolute error, mean squared error, and root mean squared error metrics are considered as the criteria for the selection of the most accurate model. LR model with R2 = 0.53, MAE = 0.0026, mean squared error (MSE) = 1.4 × 10−5, and root mean squared error (RMSE) = 0.0036 has the lowest accuracy while XGBoost with R2 = 0.8, MAE = 0.0015, MSE = 5 × 10−6, and RMSE = 0.0028 is the most accurate model. DT and RF models with R2 = 0.7 and R2 = 0.73, respectively, are in between. © The Author(s) 2025.

关键词： Mean square error

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ALL 2024 - Proceedings of the 1st Workshop on Adaptive Lifelong learning, co-located with the 25th international conference on Artificial Intelligence in Education, AIED 2024

ALL 2024 - Proceedings of the 1st Workshop on Adaptive Lifel...

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1st Workshop on Adaptive Lifelong learning, ALL 2024

The proceedings contain 5 papers. The topics discussed include: supporting personalized lifelong learning with human-centered artificial intelligence systems;using machine learning to predict the number of latent skills in online learning environments;design and development of a co-instructional designer bot using GPT-4 to support teachers in designing instruction;personalized learning in K-12 education: exploring weak-labels for a random forest-based collaborative filtering approach;and course recommendations in MOOCs using collaborative filtering and survival analysis.

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Predicting Forex Trends: A Comprehensive Analysis of Supervised learning in Exchange Rate Prediction 1st

Predicting Forex Trends: A Comprehensive Analysis of Supervi...

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1st international conference on machine learning algorithms, icmla 2024

作者： Nayak, Rudra Kalyan Sodha, Manan Mishra, Nilamadhab Tripathy, Santosh Kumar Tripathy, Ramamani Pradhan, Ashwini Kumar School of Computing Science and Engineering VIT Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India Department of Computer Science and Engineering National Institute of Technology Patna Bihar Patna India Chitkara University School of Engineering & Technology Chitkara University Himachal Pradesh India Department of Computer Science Engineering Galgotias University U.P. Greater Noida India

ISBN: (纸本)9783031758607

This research examines the coherence between regression models and forex trading concentrating on the currency pairs EUR/INR, GBP/INR, and USD/INR. Investigating the ability to dissect and forecast data using machine learning techniques exchange rates on a daily, weekly, and monthly basis using datasets collected over 11 years (January 01, 2012, to January 01, 2023) for the three distinct time frames. The research tested six supervised learning models including regression with a single independent variable (linear regression), regression using multiple independent variables (multiple regression), k-nearest neighbor, regression with decision trees, regression using random forests, and support vector regression. In certain scenarios, models provide significant accuracy for specific currency pairs and time frames. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Multiple linear regression

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Enhanced Classification Performance through Joint Mutual Information for Feature Selection in Life Science Data 1

Enhanced Classification Performance through Joint Mutual Inf...

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1st international conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Saha, Soumitra Kumar, Rakesh Gupta, Meenu Zanke, Pankaj Chandigarh University Department of Cse Mohali India Spaiens International Corporation Atlanta United States

ISBN: (纸本)9798331529246

Multifarious approaches exist for data preprocessing in machine learning (ML), pattern recognition, and data mining, and feature selection (FS) is regarded as the most efficacious technique for dimensionality reduction in a structured way. The existence of irrelevant, redundant, and disproportionate features directly diminishes the model's performance and dramatically expands its complexity. Distinguishable FS algorithms can outperform others, and the most influential aspect of a well-performing FS algorithm is discovering momentous features that help optimize model performance by processing data more efficiently. Therefore, this study introduces a novel FS technique based on the joint mutual information (FSJMI) technique for life science data. This technique declines the number of features and enhances the model's performance, or at least maintains it in most cases, making it a unique and promising approach. It also enriches predictive accuracy while minimizing computational overhead by substituting the correlation between the features and the correlation substitution of the class values with the features. Comprehensive experiments utilizing ten diverse life science datasets have been conducted to validate the suggested model's performance. The experimental results demonstrate a substantial improvement in the proposed techniques compared to other well-known FS techniques and no selection (NoSel) in the primary dataset. The results conveyed using widely used ML algorithms such as Decision Tree (DT), K Nearest Neighbour (KNN), Naive Bayes (NB) Classifier, and Logistic Regression (LR) reveal that the presented algorithm demonstrated superior performance and outperformed NoSel and other FS algorithms in mean accuracy by at least 2.7%, 3.466%, 2.038%, and 1.792%, respectively. Additionally, regarding the LR model, the proposed algorithm outperforms one of the FS models by at most 11.631%. These findings of the proposed algorithm regarding outcomes highlight its capabili

关键词： Dimensionality reduction

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Multimodality for Diagnosis of Asian Choroidal Vasculopathy: Results from a Novel Dataset and Deep-learning Experiments

Multimodality for Diagnosis of Asian Choroidal Vasculopath...

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Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and Data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on machine learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Cho, Daehyun Kim, Young Ho Ahn, Somin Oh, Jaeryung Wallraven, Christian Department of Artificial Intelligence Korea University Seoul Korea Republic of Department of Ophthalmology Korea University College of Medicine Seoul Korea Republic of Department of Ophthalmology Dongguk University Ilsan Hospital Goyang Korea Republic of Department of Artificial Intelligence and Department of Brain and Cognitive Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9783031845246

Deep learning algorithms show tremendous potential for clinical decision-making - for example, in providing automated diagnoses of imaging data. However, typical clinical datasets often are limited in size, modalities, and contain heterogeneous, incomplete data, which presents challenges for deep learning frameworks that necessitate larger, uniform datasets, complicating their deployment especially with new types of disease models. In this work, we present a case study for deep learning in such a challenging setting in the context of diagnosing Asian choroidal Vasculopathy (ACV), which is a retinopathy profile currently under discussion in ophthalmology to be differentiated from age-related macular degeneration (AMD). We first introduce a novel, human-annotated multimodal dataset for ACV versus AMD diagnosis incorporating four different imaging modalities. We next explore the usefulness of "foundation models" for this data, compared to traditional dataset-specific training. Most importantly, we investigate which of the four modalities is most discriminative and whether bi-modal classification is able to enhance performance across multiple fusion approaches. We also discuss first results of salient features using explainability techniques. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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