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automated Extraction of Domain Models from Textbook Indexes for Developing intelligent Tutoring Systems 19th

Automated Extraction of Domain Models from Textbook Indexes ...

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19th international conference on Augmented Intelligence and intelligent Tutoring Systems (ITS)

作者： Banjade, Rabin Oli, Priti Rus, Vasile Univ Memphis Memphis TN 38152 USA

ISBN: (纸本)9783031328824;9783031328831

Domain modeling is an important task in designing, developing, and deploying intelligent tutoring systems and other adaptive instructional systems. We focus here on the more specific task of automatically extracting a domain model from textbooks. In particular, this paper explores using multiple textbook indexes to extract a domain model for computer programming. Our approach is based on the observation that different experts, i.e., authors of intro-to-programming textbooks in our case, break down a domain in slightly different ways, and identifying the commonalities and differences can be very revealing. To this end, we present automated approaches to extracting domain models from multiple textbooks and compare the resulting common domain model with a domain model created by experts. Specifically, we use approximate string-matching approaches to increase coverage of the resulting domain model and majority voting across different textbooks to discover common domain terms related to computer programming. Our results indicate that using approximate string matching gives more accurate domain models for computer programming with increased precision and recall. By automating our approach, we can significantly reduce the time and effort required to construct high-quality domain models, making it easy to develop and deploy tutoring systems. Furthermore, we obtain a common domain model that can serve as a benchmark or skeleton that can be used broadly and adapted to specific needs by others.

关键词： domain modeling computer programming intelligent tutors system computer science education Java programming

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Technology Acceptance Modelling for Investigating the Uptake of Electric, Connected, Autonomous, and Shared Mobility Technologies 11

Technology Acceptance Modelling for Investigating the Uptake...

引用

11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Karathansopoulou, Konstantina Mitsiogianni, Despoina Tsaousi, Eleni Dimitrakopoulos, George Georgiadis, Dimitris Department of Informatics and Telematics Harokopio University of Athen Greece

ISBN: (纸本)9789897587450

this paper introduces QETAM (Quantitative Effect and Technology Acceptance Modelling), the first quantitative user acceptance model for evaluating the impact and adoption of Electric, Connected, Autonomous, and Shared (ECAS) mobility technologies. Developed in Python, QETAM leverages data collected through specifically designed questionnaires to assess key adoption factors, including technological reliability, user attitudes, infrastructure, and environmental considerations. the model accounts for the interconnected nature of ECAS technologies, emphasizing synergies between electric propulsion, connectivity, autonomy, and shared mobility services. Utilizing advanced statistical techniques, it analyzes large-scale datasets to provide a data-driven understanding of user behavior. Beyond academic contributions, QETAM offers practical insights for policymakers and industry stakeholders, supporting the transition toward sustainable and user-centric mobility solutions. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： engineering education

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Machine learning-Based Anomaly Detection in Smart City Traffic: Performance Comparison and Insights 11

Machine Learning-Based Anomaly Detection in Smart City Traff...

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11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Bawaneh, Mohammad Simon, Vilmos Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3 BudapestH-1111 Hungary

ISBN: (纸本)9789897587450

In recent years, urban roads have suffered from substantial traffic congestion due to the rapidly increasing number of road users and vehicles. Some traffic congestion patterns on specific roadways, such as the recurring congestion during morning and evening rush hours, can be foreseen. However, unexpected events, such as incidents, may also cause traffic congestion. Monitoring traffic status poses vital importance for city traffic operators. they can leverage the monitoring system for resource allocation, traffic lights adjusting, and adapting the public transport schedules to alleviate traffic congestion. Machine learning-based methods for anomaly detection are valuable tools for monitoring traffic status and promptly detecting congestion on city roads. In this paper, we comprehensively study the performance of the common machine learning methods for anomaly detection in the traffic congestion detection use case. In addition, we provide methods usage insights based on the study findings by examining the accuracy, detection speed, and computation overhead of the methods to guide the researchers and city operators toward a suitable method based on their needs. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Traffic congestion

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Vocational Education Development Model Based on intelligent Management System 11

Vocational Education Development Model Based on Intelligent ...

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11th international conference on Educational and Information Technology (ICEIT)

作者： Ni, Li Huang, Qiang Ye, Jingyi Hu, Bin Zhang, Ni Chang, Xiangqian Su, Zhihao Guangzhou Coll Appl Sci & Technol Guangzhou Peoples R China Guangzhou Huali Coll Guangzhou Peoples R China Natl Univ Malaysia Fac Informat Sci & Technol Kuala Lumpur Malaysia

ISBN: (纸本)9781665445467

intelligent management system centered on artificial intelligence algorithms, cloud computing technology, mobile Internet technology, and big data applications have made remarkable achievements in various industries. the intelligent management system designed in this project is to apply these technologies to explore a vocational education development model that adapts to the current Internet society. Vocational education needs to be oriented to the professional needs of the society, it needs to be oriented to students' vocational choices, and it needs to be oriented to the skill range of teachers. this project collects students' learning data and teaches students in accordance with their aptitude in the course of students' daily classes, matches students with appropriate vocational and technical elective courses and teachers, and records students' feedback during their studies, and in the future career development of students, to help students make better career choices. At the same time, the needs of students, companies, and teachers in vocational education will be collected, and artificial intelligence algorithms will be used to process student data and corporate recruitment data, automatically matching the most suitable combination of students and companies. the biggest core innovation of this system is the latest deep learning algorithm, and according to the actual situation of vocational education, the residual neural network is improved to create an improved residual neural network suitable for students' professional development recommendations. the final result of this algorithm, the auxiliary role in the process of student vocational education and development is far greater than the traditional vocational education management system, and in the C University student education and professional development database, there is about 90% satisfaction rate and the 85% occupational matching accuracy rate is far higher than the performance of the traditional vocation

关键词： intelligence management system deep learning algorithms recommendation system

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Detection and Discrimination of Arabic Phonemes Using Long Short-Term Memory (LSTM) Model 11

Detection and Discrimination of Arabic Phonemes Using Long S...

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11th IEEE international conference on intelligent Computing and Information Systems, ICICIS 2023

作者： Tolba, Rahma M. Fayed, Zaki T. Alsayadi, Hamzah A. Saad, Noureldin O. Elarif, Taha Ain Shams University Faculty of Computer and Information Sciences Computer Science Department Cairo Egypt Ibb University Faculty of Sciences Computer Science Department Yemen Canadian International Collage School of Computer Science Data Science Department New Cairo Egypt

ISBN: (纸本)9798350322101

Phonetics is a crucial branch of linguistics that studies human speech sounds and is essential for language learning, speech therapy, and speech technology development. However, current Arabic speech systems cannot instantly analyze and detect mispronunciation of Arabic phonemes, only offering a recording feature for self-evaluation. So, this paper presents a state-of-the-art approach using a Long Short-Term Memory (LSTM) model for detecting and discriminating between Arabic phonemes. the model was tested on a collected dataset for the Arabic phonemes /s/ 'sin' and /s/ 'ad and demonstrated excellent performance in discriminating between them despite their close articulation. the model achieved an accuracy of 97.33% and outperformed other techniques proposed for small datasets. the model can be utilized either independently or integrated into a computer-assisted language learning (CALL) system. Furthermore, our findings suggest that there is significant room for improvement in this area, particularly in collecting large audio datasets for all the Arabic phonemes, refining the algorithms, and optimizing the data processing pipeline. © 2023 IEEE.

关键词： Brain

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Deep learning-Based Sign Language Translator 11th

Deep Learning-Based Sign Language Translator

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11th international conference on intelligent Computing and Applications, ICRTC 2023

作者： Garg, Hardik Agarwal, Alok Arunachalam, N. Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur SRM Nagar Tamil Nadu Chennai603203 India

ISBN: (纸本)9789819717231

According to the 2011 Indian census, 13 lakh people have hearing impairments. According to the Indian National Association of the Deaf, 180 lakh individuals, or about 1% of the population in India, have hearing impairments. Our project was driven by these statistics. those with speech difficulties and deaf people require a way of connecting with the rest of the world. Some normal people are unable to recognize the symptoms of handicapped people. this results in the objective of our endeavor to convert sign language gestures into text that individuals of average intellect can read. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Remaining Cycle Time Prediction in Business Processes with TPOT Regression for automated Machine learning 4th

Remaining Cycle Time Prediction in Business Processes with T...

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4th international conference on Novel and intelligent Digital Systems, NiDS 2024

作者： Bousdekis, Alexandros Kaftantzis, Savvas theodoropoulou, Georgia Miaoulis, Georgios Department of Informatics and Computer Engineering University of West Attica Athens Greece

ISBN: (纸本)9783031733437

Process mining allows the extraction of useful information from the event logs and historical data of business processes. Predictive business process monitoring is a sub-field of process mining and deals with runtime methods aimed at generating predictive models about various objectives related to a process instance by taking advantage of Machine learning (ML) and deep learning algorithms. However, despite the large amount of research works dealing with predictions about next activity prediction and remaining time of running process instances, the objective of cycle time prediction has been overseen although it can provide higher level insights targeted the strategic planning. At the same time, automated Machine learning (AutoML) has not been investigated in the predictive business process monitoring domain in order to automate the time-consuming process of designing and optimizing ML pipelines. therefore, motivated by its promising results in other domains, we propose an approach for remaining cycle time prediction in business processes based on AutoML, and specifically on the Tree-Based Pipeline Optimization Tool (TPOT) method. the results demonstrate that the proposed approach achieves high accuracy, while it minimizes human intervention in the configuration of ML pipelines. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

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Enhancing Traffic Prediction via Spatial Multi-Granularity Co-Evolving Mechanism 11

Enhancing Traffic Prediction via Spatial Multi-Granularity C...

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11th IEEE international conference on Behavioural and Social Computing, BESC 2024

作者： Sui, Shanshan Han, Qilong Lu, Dan Wu, Shiqing Xu, Guandong Harbin Engineering University Harbin China University of Technology Sydney Sydney Australia The Education University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9798331531904

Accurate traffic prediction is crucial for effective urban planning and congestion management, aiming to forecast future traffic conditions by analyzing historical and real-time data. Traditional deep learning approaches typically rely on a single spatial granularity of traffic network data, which limits their capacity to fully capture the intricate details of multi-granularity spatial information, leading to decreased prediction accuracy in complex real-world scenarios. More critically, existing methods have shown deficiencies in effectively capturing the co-evolving dynamics across different granular levels, a key aspect for enhancing predictive accuracy. In this work, we introduce a novel Spatial Multi-granularity Co-evolving Framework (SMCF) to tackle these challenges. this innovative framework strategically integrates multiple spatial representations - micro, meso, and macro - enabling a comprehensive encapsulation of traffic dynamics at varying granularities. Unique to our approach is a co-evolutionary strategy where data from coarser granularities guide the predictive modeling at finer scales, fostering a dynamic and synergistic interaction across different levels of data granularity. Our extensive evaluations of two real-world traffic datasets confirm that the SMCF outperforms existing baseline models and demonstrates superior performance in traffic prediction tasks. this enhanced accuracy indicates our framework's ability to model complex spatial interactions and its potential applicability in real-world traffic management and planning scenarios. © 2024 IEEE.

关键词： data accuracy

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Asphalt pavement raveling identification based on machine learning 4

Asphalt pavement raveling identification based on machine le...

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4th international conference on Image Processing and intelligent Control, IPIC 2024

作者： Li, Zheng Peng, Yi Liu, Maoyi Jiang, Xin Yu, Xinyi Zhang, Zhengqi Wang, Bo Chen, Kunping Co. Ltd Chongqing400023 China Chongqing Key Laboratory of Intelligent Integrated and Multidimensional Transportation System Chongqing Jiaotong University Chongqing400074 China National and Local Joint Laboratory of Traffic Civil Engineering Materials Chongqing Jiaotong University Chongqing400074 China School of Civil Engineering Chongqing Jiaotong University Chongqing400074 China School of Resources and Safety Engineering Chongqing University Chongqing400030 China

ISBN: (纸本)9781510682313

Raveling on asphalt surfaces can lead to the occurrence of diseases such as loose potholes on the road surface. Monitoring and real-time warning of the development status of pavement raveling helps formulate road maintenance plans in time and effectively curb the occurrence and development of pavement diseases. To realize the intelligent identification of asphalt pavement raveling, the LS-40 portable 3D surface analyzer was used to collect the image data of asphalt pavement raveling and normal asphalt pavement, and the recognition effects of traditional image classification and recognition models supported vector machine (SVM), random forest and convolutional neural network (CNN) were compared. the results show that the random forest model has a good accuracy of 82% for the recognition of pavement raveling images based on the traditional machine learning model on the high-precision texture data dataset, and the convolutional neural network has an accuracy of 93%, which is 11% higher than that of random forest. the results verify the efficiency of the convolutional neural network in the identification of pavement raveling diseases. © 2024 SPIE.

关键词： Convolutional neural networks

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Artificial Intelligence for Predictive Maintenance of Port Equipment: A Revolution in Progress 10th

Artificial Intelligence for Predictive Maintenance of Port E...

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10th international conference on Design and Modeling of Mechanical Systems (CMSM)

作者： Chaibi, Maissa Daghrir, Jinen Univ Sousse Higher Inst Transport & Logist Sousse Tunisia Univ Sousse ISITCom Sousse 4011 Tunisia

ISBN: (纸本)9783031671517;9783031671524

Artificial Intelligence (AI) is playing a crucial role in enhancing predictive Maintenance for port handling equipment. By continuously collecting data, sophisticated algorithms, and machine learning capabilities, AI enables ports to accurately anticipate the maintenance needs of their equipment, thereby reducing unplanned downtime and costs. the significance of AI in this con-text lies in its ability to analyze vast amounts of data from sensors and monitoring systems of port equipment. through this analysis, predictive models can identify trends, patterns, and early signs of failure, allowing port opera-tors to proactively plan maintenance. this not only improves the safety of port operations but also maximizes operational efficiency by avoiding unexpected disruptions. Furthermore, AI also extends the equipment's lifespan by optimizing its usage and identifying parts requiring maintenance or replacement. Ultimately, this results in significant cost reducing and better resource management. In this matter, we were interested in using predictive maintenance by handling the unexpected downtimes of port equipment. this is achieved by predicting potential failure of equipment. the goal behind predictive maintenance is mainly to enhance safety, decrease costs, and making port industry more automated and intelligent.

关键词： Artificial intelligence predictive maintenance handling equipment machine learning

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