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检索条件"任意字段=International Conference on Teaching and Learning in Computing and Engineering"

共 107018 条记录，以下是361-370 订阅

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Meta-learning-Based Multimodal Fake News Detection 5th

Meta-Learning-Based Multimodal Fake News Detection

引用

5th international conference on computing and Network Communications, CoCoNet 2023

作者： Varun, M.S. Bhattacharjee, Ishaan Venkatesha, Pratheek Karike Sathvik, A. Pramath, S. Das, Bhaskarjyoti Department of Computer Science and Engineering PES University Karnataka Bengaluru India Department of Computer Science and Engineering in AI and ML PES University Karnataka Bengaluru India

ISBN: (纸本)9789819745395

In the contemporary digital era, spreading false information via social media and Internet platforms poses a significant challenge. Not only the data has become increasingly multimodal, lack of sufficient labeled data due to prohibitive labeling costs and data privacy issues poses an additional challenge. An approach such as meta-learning can help address this issue. This work showcases meta-learning with scarcely labeled data for the detection of multimodal fake news and provides a performance comparison of the meta-learning approach with the fully supervised learning approach. We leverage the power of Model-Agnostic Meta-learning (MAML) and Fast Context Adaption via Meta-learning (CAML) in this work. The results are encouraging as these meta-learning models achieve heightened accuracy and speed even with insufficient labeled data for multimodal fake news detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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Research on Student Information Literacy Evaluation using Geese Migration Optimization with K-Nearest Neighbor 1

Research on Student Information Literacy Evaluation using Ge...

引用

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

作者： Yang, Guanglan Yang, Jianhui Zhang, Zhimin Shandong Business Institute College of Information Engineering Yantai China Yantai China

ISBN: (纸本)9798331529246

Literacy is an essential capability for college students to adopt social requirements and it is significant superiority for lifelong learning and self-learning. It is better to expose information literacy learning behavior to utilize various information to understand the teaching mechanism and predicting learning results. Hence, this research proposes a Geese Migration Optimization with K-Nearest Neighbor (GMO-KNN) for the evaluation of student information literacy. The GMO enhances the feature selection performance through mimicking the decision making of geese which ensures relevant attributes are taken for literacy evaluation. This reduces the dimensionality and enhances the accuracy of KNN through eliminating the irrelevant features. The GMO prevents local optima which ensures the globalized search for optimal solutions. This combination efficiently captures the patterns in student learning behavior thereby providing accurate evaluation of information literacy level. The metrics such as accuracy, f1-score, precision and sensitivity are estimated to show performance of GMO-KNN. The GMO-KNN obtained 96.27%, 95.39%, 96.09%, 95.87% of accuracy, f1-score, precision and sensitivity which is better than existing methods. © 2025 IEEE.

关键词： Contrastive learning

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A Preliminary Study of the Stressed and Drowsy Driving Prediction Models using Semi-Supervised learning 24

A Preliminary Study of the Stressed and Drowsy Driving Predi...

引用

8th international conference on Algorithms, computing and Systems, ICACS 2024

作者： Chui, Kwok Tai Liu, Jiaqi Hung, Kevin Wu, Ho Chun Zhao, Mingbo School of Science and Technology Hong Kong Metropolitan University Hong Kong Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong School of Information Science and Technology Donghua University Shanghai China

ISBN: (纸本)9798400718304

Predicting the occurrence of stressed and drowsy driving provides sufficient time to alert drivers and prevent undesired driving behaviours that result in road traffic accidents. Typically, the predictive machine learning models for stress and drowsy driving are formulated using supervised learning with labelled data. Labelled data are available only in training datasets, where labelling new data becomes infeasible and costly. With the ever-growing quantity of new and unlabelled data, once the model is deployed, unlabelled data quickly dominate the whole data population, and bias toward unlabelled data may increase the error rate of the prediction model. A preliminary study was thus conducted to train the predictive model using semi-supervised learning. We analyze and compare the performance of four algorithms, namely extreme gradient boosting (XGBoost), autoregression (AR), autoregressive integrated moving average (ARIMA), and exponential smoothing (ES). Various portions of unlabeled data were introduced, and more window sizes (in advance intervals) were chosen for the analysis. © 2024 Copyright held by the owner/author(s).

关键词： Self-supervised learning

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Detection of Periapical Lesions in Dental X-rays and Oral Scan using Computer Vision Model 6

Detection of Periapical Lesions in Dental X-rays and Oral Sc...

引用

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

作者： Brintha, N.C. Gurukiran, D. Deekshith, N. Mahidhar, D. Sanjay, Dayina Dept of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu Virudhunagar India

ISBN: (纸本)9798331522667

Periapical lesions are abnormal growths or infections that develop close to the tip of a tooth's root and can be caused by oral trauma, decaying teeth, or dental work done in the past. It is particularly difficult to accurately identify and differentiate periapical lesions, which presents a major diagnostic problem. Density measurements are not reliable and can't be linked to histology. Yolov5 which detect objects in real time with great accuracy is used to automatically identify and categorize lesions from imaging data. This model is a good object detector to detect small objects. It is the fastest model compared to other models. © 2025 IEEE.

关键词： Deep learning

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Expert System for teaching Classification Systems Workflow 17th

Expert System for Teaching Classification Systems Workflow

引用

17th Workshop of Hybrid Intelligent Systems, HIS 2024, 17th Workshop on Intelligent learning Environments, WILE 2024 and 6th Workshop on New Trends in Computational Intelligence and Applications, CIAPP 2024, held in conjunction with Mexican international conference on Artificial Intelligence, MICAI 2024

作者： Sánchez-Sánchez, Christian Departamento de Tecnologías de la Información Universidad Autónoma Metropolitana Unidad Cuajimalpa Mexico City Mexico

ISBN: (纸本)9783031838811

Artificial intelligence (AI) is increasingly present in our lives, and it is generating a higher demand for human resources with knowledge about this area of computing. It is essential to have tools or resources that help in learning AI. This article presents an expert system that supports teaching the immersed process (workflow) in developing systems that use machine learning, particularly classification. This system is based on rules and preconditions guiding the development process, validating that the functions are executed logically and indicating errors made. The system architecture allows users to add or remove functions and precondition rules. One key role of the system is to guide users in identifying and solving errors. The qualitative evaluation showed that the students, after using the system for a few weeks, were able to confidently identify and solve errors when implementing practices that involved this type of development process. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Expert systems

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Research on AI-Driven teaching of Medical Data Mining Courses Under the BOPPPS Model 14

Research on AI-Driven Teaching of Medical Data Mining Course...

引用

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

作者： Han, Huirui Zhan, Heqing Liu, Wei College of Biomedical Information and Engineering Hainan Medical University Haikou China

ISBN: (纸本)9798331540883

As interdisciplinary integration deepens, the demand for applying engineering principles to address biological and medical challenges is on the rise. While machine learning serves as a crucial tool for processing complex medical data, the effective application of its techniques remains a significant challenge. Therefore, the 'Medical Data Mining' course is designed to cultivate students' theoretical and practical skills in biomedical engineering. Current pedagogical practices are hindered by excessive teacher dominance and insufficient student interaction. To address these issues, this study introduces generative artificial intelligence technologies and interactive teaching models, integrated with the BOPPPS instructional design framework. This approach is designed to stimulate deep thinking, enhance student engagement and knowledge retention, and foster the development of critical and creative thinking skills among learners. © 2025 IEEE.

关键词： Students

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Applications of Cloud computing and Artificial Intelligence in the Oil and Gas Industry: A Review 6

Applications of Cloud Computing and Artificial Intelligence ...

引用

6th IEEE international conference on Image Processing, Applications and Systems, IPAS 2025

作者： Jellah, Alfitouri Ibrahim Mohamed, M. Moaad, I. Bani-Waleed University Oil and Gas Engineering Department Bani-Waleed Libya

ISBN: (纸本)9798331506520

This paper reviews the latest machine learning and artificial intelligence technology, showcasing its potential to revolutionize the oil and gas industry. Despite the current limitations, the industry is on the brink of a digital transformation that promises to enhance operations and increase overall production. With the help of AI and cloud computing, the oil and gas industry's future is one of optimism and potential. The review showed a significant increase in production when applying AI technology, particularly with EOR. The paper summarized the limitations and future challenges of this technology. © 2025 IEEE.

关键词： Gas industry

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Delay-Optimized Task Scheduling Based on Deep Reinforcement learning in computing Power Networks 3rd

Delay-Optimized Task Scheduling Based on Deep Reinforcement ...

引用

3rd international conference on Machine learning, Cloud computing and Intelligent Mining, MLCCIM 2024

作者： Tang, Qinqin Xu, Deyun Xie, Renchao Feng, Li Chen, Tianjiao Huang, Tao Beijing University of Posts and Telecommunications Beijing China China Mobile Research Institute Beijing China

ISBN: (纸本)9789819624676

With the rapid advancement of information and communication technology, there arises an increasing demand for heightened computing capabilities to facilitate a myriad of intelligent services. The computing power network (CPN) amalgamates an array of resources including cloud and edge devices to enable collaborative sensing, routing, and scheduling. This integration significantly amplifies the utilization of computing network resources while concurrently augmenting service quality. In the realm of multi-node distributed joint computing within CPNs conventional scheduling methodologies frequently neglect the interplay between computing and the network, resulting in suboptimal task scheduling. To address this issue, this study proposes a delay-optimized task scheduling mechanism based on deep reinforcement learning. Simulation results demonstrate that this framework achieves faster convergence speed, particularly under dual-depth networks, outperforming other algorithms in reducing system delays, energy and enhancing scheduling efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： computing power

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Next-Generation Intrusion Detection Systems with Machine learning and Deep learning-Based Firewalls 3

Next-Generation Intrusion Detection Systems with Machine Lea...

引用

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

作者： Rani, Ritu Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331515911

The growing complexity of cyber-attacks has underlined the requirement of strong Intrusion Detection Systems (IDS) able to instantly recognize a variety of hazards. This work offers a strategy for improving Next-Generation Intrusion Detection Systems (NGIDs) combined with firewalls by use of machine learning (ML) and deep learning (DL) approaches. Using the NSL-KDD dataset - which includes both normal and attack network traffic - the work creates a system able to classify traffic patterns into "Normal"and "Attack"categories. The proposed method consists in four phases: data collecting, data preprocessing, feature selection and extraction, and classifier application. In the preprocessing stage, the dataset is cleaned and normalized;hence, suitable feature selection follows to increase classification accuracy. Trained and tested for classification using performance criteria including accuracy, precision, recall, and F1-score, naïve bayes and support vector machine (SVM) models are then compared in efficacy. The results reveal that the SVM classifier has low misclassification and great accuracy (97.29%), therefore proving its prospective use in advanced IDS. The article underlines how several models could be merged inside a firewall system to offer real-time network protection. Future studies will look at using deep learning techniques and ensemble methods to raise detection accuracy even more, therefore allowing NGIDS to be more adaptable and efficient in countering evolving cyber threats. © 2025 IEEE.

关键词： Network intrusion

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Research on human pose and action detection algorithm based on YOLOv8 2

Research on human pose and action detection algorithm based ...

引用

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

作者： Cheng, Weizhong Wang, Xuefei School of Information Engineering Suihua University Heilongjiang Suihua China School of Electrical Engineering Suihua University Heilongjiang Suihua China

ISBN: (纸本)9781510689053

Human pose and action detection technology has significant applications in areas such as smart security, sports monitoring, and human-computer interaction. The development of deep learning has driven the demand for real-time and efficient detection algorithms in this domain. Although existing human action detection algorithms have improved accuracy, their model complexity and computational demands hinder practical use in portable devices and edge computing platforms. To address this, the paper presents a lightweight human pose and action detection algorithm based on the YOLOv8 framework, designed to meet the high-precision, low-latency needs of edge computing and portable devices. In the feature extraction phase, GhostNet's lightweight structure is used to reduce model parameters, while the CBAM attention mechanism, applied in the feature fusion phase, enhances the model's attention to critical features. This approach significantly decreases computational load while maintaining high accuracy. Experimental results indicate that the algorithm achieves a real-time detection rate exceeding 40 frames per second on edge computing platforms, demonstrating high accuracy and robustness across various human action scenarios. This offers strong technical support for human pose detection in smart security and wearable devices. © 2025 SPIE.

关键词： Portable equipment

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