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Potential effects of a role-playing digital gaming learning system on the learning performance and motivation in a humanities course

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Interactive Learning Environments 2023年第8期31卷 5078-5091页

作者： Chin, Kai-Yi Chen, Yen-Lin Department of Data Science Soochow University Taipei City Taiwan Department of Computer Science Information Engineering National Taipei University of Technology Taipei City Taiwan

Using digital learning content to realize learning in games is a rapidly-developing direction of interest for teachers and researchers. This study has developed a digital role-playing gaming system to review Social Studies course content to a fifth Grade class at an elementary school. It allows students to experience the historical storyline of Tamsui, located in the north of Taiwan, by playing the role of Dr. Mackay. An experiment was conducted to evaluate the potential effects of the proposed system when applied to the reviewing activity of Humanities courses. The results of the post-test scores showed that the proposed system is not superior to the traditional teaching method which uses PowerPoint-based slides with the teacher’s oral instruction, but it still enhances their overall learning performance after the reviewing activity. Moreover, the results of the questionnaire survey showed that the students who reviewed by using the proposed system showed a higher learning motivation than those reviewing with the traditional teaching method. It is therefore concluded that our role-playing digital gaming system can be used to assist the teachers’ teaching activities, because it allows the students to complete their reviewing activity by themselves, and it effectively enhances their learning performances and motivation. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Digital game-based learning elementary education humanities course role-playing game technology-enhanced learning

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Guardians of Truth: Exploring Techniques for Veracity Identification in social media during an Infodemic 3

Guardians of Truth: Exploring Techniques for Veracity Identi...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Suthanthiradevi, P. Revathy, G. Sathish Kumar, P. Karthikeyan, B. Muthu, Lakshmi.V SRM institute of Science and Technology Data Science and Business Systems School of Computing India Vels Institute of science Technology and Advanced studies Department of Computer science and engineering India Rajalakshmi institute of technology Computer and Communication Engineering India SASTRA Deemed University School of Computing India St. Joseph's Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350372120

With the recent advent of technology, social networks are accessible 24/7 using mobile devices. During covid-19 pandemic the propagation of misinformation are mostly related to the disease, its cures and prevention. With Twitter as the medium this information was spreaded to millions of people who lack domain specific knowledge. This can lead to bloster fear and direct damage to the people. This research focuses on identifying the veracity of the Twitter posts behind pandemic situation. The authors have proposed a Transformer Model using Bat Algorithm for identifying the fake news. This model is trained and evaluated using infodemic metadata. Our experimental results shows by which proposed model accurately detected the fake news and achieved 96.5% of f1-score. © 2024 IEEE.

关键词： Social networking (online)

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Evaluation of Logistic Regression and Decision Tree Models for Lung Cancer Prediction Using Survey data

Evaluation of Logistic Regression and Decision Tree Models f...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Narayana, K.E. Jayashree, K. ThavaVinu, A. Sindhuja, M. Jaeyalakshmi, M. Rajalakshmi Engineering College Department of Information Technology Tamilnadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India Prathyusha Engineering College Department of Information Technology Chennai India Acharya University Department of Computer Science and Engineering Karakul Uzbekistan Rajalakshmi Engineering College Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798331522988

Lung cancer is one of the most deadly and ubiquitous forms of cancer globally. Early detection can make a significant difference in survival rates, prognosis, etc. Background The present study compares the performance of logistic regression and decision trees using a survey-based dataset for predicting lung cancer. This dataset has a total of 284 cases with 16 features;behavioral patterns, symptomatic data, and demographic details. Logistic regression is a binary classification algorithm that uses input features to model the probability of lung cancer presence given these input features using for example a sigmoid function. Using Decision Trees can identify significant fields in a dataset while reducing Gini Impurity for models that categorize patients based on feature values. The performance of both models is evaluated according to accuracy, F1-score, precision, and recall measures. From the preliminary results, it looks like the Decision Tree works a little better than the logistic regression model which gets an accuracy of 94%, but this one does with more accuracy (97%). This result emphasizes the inclusion of logistic regression within this context, as it may be able to predict lung cancer with greater accuracy. Additionally, to ensure the generalization and robustness of the model, further investigation and trials will focus on optimizing performance through ensemble approaches, hyperparameter tuning, and cross-validation. This comparative analysis demonstrates the effectiveness of machine learning techniques in developing prediction models for early lung cancer detection, an important step toward a quick and appropriate intervention. © 2024 IEEE.

关键词： Lung cancer

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Triple voting: hybrid cardiovascular diseases prediction model

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International Journal of Applied Decision sciences 2025年第3期18卷 257-281页

作者： Solomon, Dahlak Daniel Kumar, Karan Sonia Kanwar, Kushal Polat, Kemal Yogananda School of AI Computers and Data Science Shoolini University Himachal Pradesh Solan India Mullana Ambala133207 India Department of Computer Science and Engineering and Information Technology Jaypee University of Information and Technology Himachal Pradish Solan India Department of Electrical and Electronics Engineering Bolu Abant Izzet Baysal University Bolu Turkey

Currently, cardiovascular diseases are a high-risk cause of death in both developed and developing countries. Thus, heart disease prognosis has received substantial interest in the medical field worldwide. The incidence of heart disorders is escalating at an alarming rate, and it is crucial and worrisome to anticipate their occurrence. Predicting and detecting cardiovascular disease using machine learning and data mining might be clinically useful, but difficult. There are numerous machine learning algorithms accessible, several studies have developed machine learning algorithms for early cardiac disease prediction to help physicians suggest medical treatments. The accuracy of the model will be evaluated to determine whether the performance of the model is accurate or not. Seven machine learning methods are compared in this study, with the data obtained from the UCI Laboratory’s cardiovascular patient database. In essence, this research presents a majority voting-based hybrid model which is called triple voting. The hybrid model uses voting of Naïve Bayes, logistic regression (LR) and support vector machines (SVM) experimental outcomes show the proposed triple voting model’s accuracy is 89%, which is higher than the individual models and other proposed hybrid models. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Pulmonary diseases

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Robust EEG-based Emotion Recognition Using an Inception and Two-sided Perturbation Model 46

Robust EEG-based Emotion Recognition Using an Inception and ...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Sartipi, Shadi Cetin, Mujdat University of Rochester Department of Electrical and Computer Engineering Rochester United States University of Rochester Goergen Institute for Data Science Department of Electrical and Computer Engineering Rochester United States

ISBN: (纸本)9798350371499

Automated emotion recognition using electroencephalogram (EEG) signals has gained substantial attention. Although deep learning approaches exhibit strong performance, they often suffer from vulnerabilities to various perturbations, like environmental noise and adversarial attacks. In this paper, we propose an Inception feature generator and two-sided perturbation (INC-TSP) approach to enhance emotion recognition in brain-computer interfaces. INC-TSP integrates the Inception module for EEG data analysis and employs two-sided perturbation (TSP) as a defensive mechanism against input perturbations. TSP introduces worst-case perturbations to the model's weights and inputs, reinforcing the model's elasticity against adversarial attacks. The proposed approach addresses the challenge of maintaining accurate emotion recognition in the presence of input uncertainties. We validate INC-TSP in a subject-independent three-class emotion recognition scenario, demonstrating robust performance. © 2024 IEEE.

关键词： Emotion Recognition

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CylE: Cylinder Embeddings for Multi-hop Reasoning over Knowledge Graphs 17

CylE: Cylinder Embeddings for Multi-hop Reasoning over Knowl...

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17th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2023

作者： Nguyen, Chau Duc Minh French, Tim Liu, Wei Stewart, Michael ARC Centre for Transforming Maintenance through Data Science Department of Computer Science and Software Engineering The University of Western Australia

ISBN: (纸本)9781959429449

Recent geometric-based approaches have been shown to efficiently model complex logical queries (including the intersection operation) over Knowledge Graphs based on the natural representation of Venn diagram. Existing geometric-based models (using points, boxes embeddings), however, cannot handle the logical negation operation. Further, those using cones embeddings are limited to representing queries by two-dimensional shapes, which reduced their effectiveness in capturing entities query relations for correct answers. To overcome this challenge, we propose unbounded cylinder embeddings (namely CylE), which is a novel geometric-based model based on three-dimensional shapes. Our approach can handle a complete set of basic first-order logic operations (conjunctions, disjunctions and negations). CylE considers queries as Cartesian products of unbounded sector-cylinders and consider a set of nearest boxes corresponds to the set of answer entities. Precisely, the conjunctions can be represented via the intersections of unbounded sector-cylinders. Transforming queries to Disjunctive Normal Form can handle queries with disjunctions. The negations can be represented by considering the closure of complement for an arbitrary unbounded sector-cylinder. Empirical results show that the performance of multi-hop reasoning task using CylE significantly increases over state-of-the-art geometric-based query embedding models for queries without negation. For queries with negation operations, though the performance is on a par with the best performing geometric-based model, CylE significantly outperforms a recent distribution-based model. © 2023 Association for Computational Linguistics.

关键词： Knowledge graph

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HLMEA: Unsupervised Entity Alignment Based on Hybrid Language Models 39

HLMEA: Unsupervised Entity Alignment Based on Hybrid Languag...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Jin, Xiongnan Wang, Zhilin Chen, Jinpeng Yang, Liu Oh, Byungkook Hwang, Seung-Won Li, Jianqiang National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Alibaba Group China School of Computer Science Beijing University of Posts and Telecommunications China Xiangjiang Laboratory China School of Computer Science and Engineering Central South University China Computer Science & Engineering Konkuk University Korea Republic of Department of Computer Science and Engineering Seoul National University Korea Republic of

ISBN: (纸本)157735897X

Entity alignment (EA) is crucial for integrating knowledge graphs (KGs) constructed from diverse sources. Conventional unsupervised EA approaches attempt to eliminate human intervention but often suffer from accuracy limitations. With the rise of large language models (LLMs), leveraging their capabilities for EA presents a promising direction. However, it introduces new challenges: formulating the LLM-based EA problem and extracting the background knowledge in LLMs to realize EA without human intervention. This paper proposes HLMEA, a novel hybrid language model-based unsupervised EA method. HLMEA formulates the EA task into a filtering and single-choice problem and synergistically integrates small language models (SLMs) and LLMs. Specifically, SLMs filter candidate entities based on textual representations generated from KG triples. Then, LLMs refine this selection to identify the most semantically aligned entities. An iterative self-training mechanism allows SLMs to distill knowledge from LLM outputs, enhancing the EA ability of hybrid language models in subsequent rounds cooperatively. We also conducted extensive experiments on benchmark datasets to evaluate HLMEA's performance. The results demonstrate that HLMEA significantly outperforms unsupervised and even supervised EA baselines, proving its potential for scalable and effective EA across large KGs. The code and data are available at https://***/xnjin-ai/HLMEA. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Artificial Vision - Yolov8 Algorithm for Objects and Impediments Detection 2

Artificial Vision - Yolov8 Algorithm for Objects and Impedim...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Lakshmi, S. Jothi Sasirekha, S.P. Mekala, N. Reshma, R. Nair, V. Indu Bhagya, S.G. Nehru Institute of Technology Department of Information Technology Coimbatore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Coimbatore India SNS College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India Bapuji Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Davangere India

ISBN: (纸本)9798331534950

This paper presents an object detection method using YOLO technique (You Only Look Once) based deep learning algorithm to help visually impaired people in their daily life. A Cobotic Spectacle is a cutting-edge product designed specifically for visually impaired individuals. The system seamlessly integrates face motion recognition, object identification and text detection functionalities using YOLO Algorithm. The real time object detection method uses YOLO and convolution neural networks (CNN) for single forward propagation from end to end. The proposed system utilizes YOLOv8, a state-of-the-art real-time object detection algorithm, which is trained on the Common Object in Context (COCO) dataset to accurately identify objects in front of a person. With a high degree of accuracy, the system then conveys the identified object information through a synthesized voice output, thereby enhancing visual perception and providing an immersive *** face motion recognition module promotes improved social interaction, while object identification increases environmental awareness. This integrated solution combines edge processing and cloud-based analysis for portability, real-time processing, and computational efficiency. A Cobotic Spectacle empowers visually impaired individuals, providing a state-of-the-art smart spectacle system that enhances visual perception, promotes independence, and improves their overall quality of life. © 2024 IEEE.

关键词： Convolutional neural networks

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Integrated Leaf Disease Recognition Across Diverse Crops through Transfer Learning 5

Integrated Leaf Disease Recognition Across Diverse Crops thr...

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5th International Conference on Innovative data Communication Technologies and Application, ICIDCA 2024

作者： Tambe, Utkarsh Yashwant Shanthini, A. Hsiung, Pao-Ann Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Computer Science and Information Engineering National Chung Cheng University Chiayi621301 Taiwan

Global food security is seriously threatened by plant diseases and can cause severe economic losses to farmers. Automated detection of plant diseases using computer vision and machine learning techniques has become a popular research area due to its potential to provide faster and more accurate results than traditional methods. In this research, we propose a plant disease detection model based on transfer learning using the MobileNetV2. We evaluate the suggested method on a dataset consisting of 38 various kinds of diseases across 14 different plants. Our experimental findings indicate that the proposed method has a typical accuracy of 91.98% and outperforms additional cutting-edge CNN models for plant disease detection. The experimental findings support the suggested approach's validity and show that it effectively detects plant diseases. We also have put forth the evaluation metrics to investigate the model. The suggested method has potential applications in real-world scenarios and can help farmers detect diseases in their crops at an early stage, allowing them to take timely action to minimize crop losses. © 2024 The Authors. Published by Elsevier B.V.

关键词： Losses

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Few-Shot Learning-Based Machine Fault Diagnosis Using EMD-Gammatone Spectrogram with Limited Labeled Audio dataset

Few-Shot Learning-Based Machine Fault Diagnosis Using EMD-Ga...

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2025 IEEE International Conference on Big data and Smart Computing, BigComp 2025

作者： Zabin, Mahe Ayon, Syed Tasnimul Karim Siraj, Farhan Md Shuvo, Mehedi Hasan Choi, Ho-Jin Uddin, Jia Korea Advanced Institute of Science and Technology School of Computing Daejeon Korea Republic of BRAC University Department of Computer Science and Engineering Dhaka Bangladesh Dhaka University of Engineering & Technology Department of Computer Science and Engineering Dhaka Gazipur Bangladesh Woosong University AI and Big Data Department Endicott College Daejeon Korea Republic of

The deep learning models are computationally burdensome, and their accuracy depends on the number of labeled datasets used in training. The scarcity of the labeled dataset in industrial machine diagnosis is a significant concern as it is pretty impossible to collect data from faulty machines in industrial environments. To address this issue, in this paper, we presented a few-shot learning-based machine fault diagnosis model on a lightweight SqueezeNet architecture using generated texture images from audio sensor data. Subsets of two public audio datasets, MIMII and ToyADMOS, containing limited data- 10 images per class were used to validate the model. The experimental results demonstrate that the model has exhibited 89.6% and 98.3% accuracy for MIMII and ToyADMOS, respectively, with only ten labeled images of each fault class. As the model has only 73 thousand trainable parameters and requires only 300 KB of memory, it is expected to be deployable in a portal device for machine fault diagnosis. © 2025 IEEE.

关键词： Deep learning

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