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

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Revolutionizing Education: Generative AI as a Catalyst for Personalized learning and Innovative teaching Practices 5

Revolutionizing Education: Generative AI as a Catalyst for P...

引用

5th international conference on Advanced Research in computing, ICARC 2025

作者： Jayaweera, I.G.U.D. University of Kelaniya Faculty of Graduate Studies Sri Lanka

ISBN: (纸本)9798331530983

Generative AI is transforming education by providing innovative tools for personalized learning, content creation, and teaching assistance. This paper explores the potential of generative AI in enhancing teaching methodologies and learning experiences, addressing challenges, and proposing solutions for its effective integration into education systems. The generative AI tool, ChatGPT, has captivated global attention with its remarkable ability to perform sophisticated and complex tasks. Its advanced capabilities, particularly within the field of education, have elicited mixed reactions among educators, as this breakthrough in AI technology is poised to revolutionize traditional educational practices. This exploratory study synthesizes recent literature to examine the potential benefits and challenges of ChatGPT in enhancing teaching and learning. On the positive side, ChatGPT offers opportunities to foster personalized and interactive learning experiences, support the creation of prompts for formative assessments, and provide ongoing feedback to inform teaching and learning processes. However, the study also identifies significant limitations inherent to ChatGPT, such as the generation of inaccurate information, potential biases stemming from its training data, and privacy concerns that may exacerbate existing inequities. To ensure the constructive and safe integration of ChatGPT into education, the study presents recommendations for its effective use. These include encouraging collaboration among policymakers, researchers, educators, and technology experts to address the risks and ethical implications of generative AI. By initiating proactive discussions and developing comprehensive guidelines, stakeholders can harness the potential of ChatGPT to enhance education while safeguarding the interests of learners and educators alike. © 2025 IEEE.

关键词： Students

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Cryptography Innovations for Securing Data in the Quantum computing Era: Integrating Machine learning for Enhanced Security

Cryptography Innovations for Securing Data in the Quantum Co...

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2025 international conference on Computer, Electrical and Communication engineering, ICCECE 2025

作者： Temara, Sheetal Bhagyalakshmi, L. Suman, Sanjay Kumar Shakunthala, M. Chitra, N. Thulasi Golla, Kishor University of the Cumberlands Department of Computer and Information Sciences United States Rajalakshhmi Engineering College Dept. of ECE TN Chennai India St. Martin's Engineering College Dept. of Ece and Dean R&d Telangana Secunderabad India R.M.D. Engineering College Dept. of ECE TN Thiruvallur India Mlr Institute of Technology Dept. of CSE Hyderabad India St. Martin's Engineering College Dept. of CSE Telangana Secunderabad India

ISBN: (纸本)9798331513894

The rise of quantum computing introduces substantial risks to traditional cryptographic protocols, which are vulnerable to quantum decryption methods such as Shor's algorithm. To address these emerging threats, this paper proposes an innovative cryptographic framework that integrates machine learning (ML) techniques to enhance data security in the quantum computing era. Our approach leverages ML-driven anomaly detection, adaptive key management, and predictive analytics to create a flexible and resilient cryptographic defense. The system's anomaly detection module utilizes neural networks to identify potential quantum-based decryption attempts, while reinforcement learning optimizes key generation and distribution in response to detected threats. Experimental results demonstrate that the proposed ML-augmented framework significantly improves anomaly detection accuracy and reduces vulnerability to quantum decryption attempts by dynamically adjusting cryptographic parameters. These findings underscore the potential of machine learning to strengthen cryptographic systems, making them adaptable to the advanced threats posed by quantum computing. © 2025 IEEE.

关键词： Predictive analytics

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Fast reinforcement learning 2nd

Fast reinforcement learning

引用

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

作者： Sahni, Sartaj University of Florida United States

ISBN: (纸本)9781032738659

Reinforcement learning (RL) is a highly effective approach for teaching intelligent agents to make decisions in challenging and ever-changing contexts. With the increasing use of RL in real-world situations, there is a rising demand for rapid reinforcement learning to improve effectiveness, decrease training duration, and facilitate immediate decision making across many fields. Fast Reinforcement learning can be achieved through various methods, one of which is Parallelization. This involves splitting RL computations across numerous processors or GPUs to take advantage of parallel processing capabilities, resulting in a large reduction in training time. Another method involves utilizing specialized hardware such as GPUs and TPUs that can enhance the speed of RL training by executing parallel computations more effectively than conventional CPUs. Developing and executing RL algorithms that are designed to be more efficient and have lower computational complexity leads to quicker learning. Cache memory is also essential in accelerating reinforcement learning by minimizing the access time for frequently utilized data. It enhances the performance of RL algorithms by storing and retrieving crucial information, such as state-action pairs and Qvalues. Cache memory usage optimizes data access patterns, resulting in reduced latency and improved speed of learning rounds. The landscape of fast reinforcement learning will be shaped by overcoming challenges such as transfer learning and meta-learning, algorithmic complexity, scalability to handle data sets of complex environments, generalization, etc. will continue to shape the landscape of fast reinforcement learning. © 2025 The Author(s).

关键词： Reinforcement learning

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A Comprehensive Review of Soft computing Enabled Techniques for IoT Security: State-of-the-Art and Challenges Ahead 5th

A Comprehensive Review of Soft Computing Enabled Techniques ...

引用

5th international conference on Data Science, Machine learning and Applications

作者： Parabrahmachari, Sriram Narayanasamy, Srinivasan Sathyabama Inst Sci & Technol Dept Comp Sci & Engn Chennai 600119 Tamil Nadu India Rajalakshmi Engn Coll Dept Comp Sci & Engn Chennai 600119 Tamil Nadu India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

In the past ten years, the Internet of Things (IoT) has become a massive force in our daily lives, providing countless smart services that have improved human existence. Nevertheless, the increased availability and rapidly escalating demand for intelligent devices and networks mean that IoT is currently encountering unprecedented security challenges. While there are established security protocols in place for IoT, these conventional methods fall short in effectively addressing the escalating frequency and severity of attacks. The term 'security' is used to encompass various concepts, including integrity, confidentiality, and privacy. Therefore, a strong and up-to-date security system is necessary for the next generation of industrial IoT. One area of technological advancement that can address the ongoing and future challenges of IoT security is Soft computing. It has opened up many possible research avenues for detecting attacks and identifying abnormal behaviors in smart devices and networks. This paper explores the architecture of IoT and provides a comprehensive literature review of Soft computing based approaches to IoT security, including the different types of possible attacks. It also presents potential Soft computing-based solutions for IoT security and discusses open research issues along with future research scope.

关键词： Internet of Things (IoT) Security and privacy Soft computing Attacks computing methodologies

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Cross-Domain Adaptation Through Adversarial teaching for Unmanned Ariel Vehicles in Smart Cities 22

Cross-Domain Adaptation Through Adversarial Teaching for Unm...

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22nd international learning and Technology conference, L and T 2025

作者： Mazhar Ul Haq, Qazi Yao, Leethar Lin, Bo-Yu Yuan Ze University Dept. of IBPI and Computer Science and Engineering Zhongli Taiwan National Taipei University of Technology Dept. of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9798331532215

Person re-identification has been an important issue of surveillance systems in smart cities. However, this requires huge datasets to supervise deep learning models for accurately identifying and tracking people in smart cities. In this paper, we propose adversarial learning in integration with a mutual mean teaching model known as adversarial teaching to take the issue of domain adaptation. Initially, they learn a source dataset in a supervised way. This model information is then transferred to another model through unsupervised learning and pseudo-labels are generated by generative adversarial learning. The multi-class invariance is handled by adversarial learning. The performance of the proposed model is evaluated on two different datasets source and target datasets. Multiple experiments on Duke and Market datasets indicate higher accuracy of F-1 score on both datasets compared to previous methods, demonstrating the superior performance of the proposed method. © 2025 IEEE.

关键词： Adversarial machine learning

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learning in the Digital Space: A Review of Assessment Approaches in Education Metaverse 5th

Learning in the Digital Space: A Review of Assessment Approa...

引用

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

作者： Abdul Halim, Nur Ameerah Ismail, Ajune Wanis ViCubeLab Faculty of Computing Universiti Teknologi Malaysia Johor Johor Bahru Malaysia

ISBN: (纸本)9789819747108

The advent of the education metaverse has transformed the landscape of learning and teaching, offering immersive and dynamic. The Education Metaverse represents a transformative paradigm shift in the field of education, leveraging immersive technologies to provide dynamic and interactive learning experiences. This paper explores various aspects of the education Metaverse, including conceptual frameworks, pedagogical approaches, and assessment approaches, drawing insights from recent research works in this emerging field. The findings reveal a growing interest in immersive and personalized learning, augmented by innovative assessment methodologies that go beyond traditional testing. The paper concludes by highlighting key areas for future research and development, emphasizing the need for continued exploration of this promising educational frontier experiences for learners. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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Quantales: Bridging Realms with Quantum Generative Adversarial Networks and Transformers in Educational Content Creation 3

Quantales: Bridging Realms with Quantum Generative Adversari...

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

作者： Meeradevi Sriraksha, P.J. Vishal, Rahul K. Kulkarni, Pooja S. Ramaiah Institute of Technology Department of Artificial Intelligence and Machine Learning Bangalore India

ISBN: (纸本)9798331515911

Traditional teaching methods in Indian schools often lack engaging elements, hindering students' full comprehension of concepts and fostering a tendency to memorize rather than understand. Existing solutions fail to provide effective visualization-based interactive learning. To address these issues, we propose an interactive application-based solution utilizing generative AI image generation models that can be used by students and teachers. Our application employs interactive story generation, allowing students to prompt creative texts and gain a deeper understanding of concepts. Trained on the school curriculum, this tool facilitates self-study, empowering students to understand complex topics independently. The application extends to classroom use, enabling teachers to incorporate interactive and application-based learning alongside traditional methods. Leveraging Quantum Generative Adversarial Networks (QGANs) and transformer-based models, the system analyses user input, extracts key elements, and creates dynamic story graphs for engaging narrative experiences. The integration of quantum computing enhances the application's capabilities, providing a novel approach to education through dynamic *** conclusion, the proposed solution seeks to bridge the gap in traditional teaching methods by introducing an innovative and interactive approach to education using Quantum Generative Adversarial Networks. This holistic solution addresses the limitations of current educational practices, offering a transformative learning experience for students and teachers alike. © 2025 IEEE.

关键词： Generative adversarial networks

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Machine learning-based approaches for the diagnosis and detection of brain stroke 2nd

Machine learning-based approaches for the diagnosis and dete...

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

作者： Panchal, Trupti Ramasubramanian, Sugayaalini Kumar, Yogesh Department of Mechanical Engineering Pandit Deendayal Energy University Gujarat Gandhinagar India Department of Computer Science and Engineering Pandit Deendayal Energy University Gujarat Gandhinagar India

ISBN: (纸本)9781032738659

This paper presents an in-depth analysis on brain stroke prediction using machine learning based approaches for prediction and classification. We take on a wide range of data sources, including demographic data, medical records, lifestyle conditions, and clinical measures for a prediction model. We managed to determine significant patterns and risk factors related to stroke owing to the dataset utilised in this study. Feature selection techniques are utilised to identify the most relevant predictors, enhancing the efficiency and interpretability of the model. In addition, we study alternative data preparation methods, such as managing missing values, outlier identification, and feature scaling, to ensure data quality and model resilience. © 2025 The Author(s).

关键词： Forecasting

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Technical Concepts on Cloud Security and Privacy Using Deep learning Techniques and Hybridized Encryption 5th

Technical Concepts on Cloud Security and Privacy Using Deep ...

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5th international conference on Data Science, Machine learning and Applications

作者： Rajchandar, K. Madanan, Mukesh Sastry, R. V. L. S. N. Saxena, Monika SR Univ Sch CS & AI Warangal Telangana India Dhofar Univ Dept Comp Sci Salalah Oman GIET Univ Sch Comp Sci & Engn Gunupur Orisha India Banasthali Vidyapith Dept Comp Sci Banasthali India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

The current research questions that are raising concern security in cloud computing. Cloud storage is replacing traditional data storage in numerous companies because it provides instant knowledge of data from any location. However, the main problem stopping businesses from utilizing cloud computing is data security. Cloud computing has been one of the most talked about subjects in technology lately due to its capacity to manage a large number of resources and data. One of the biggest obstacles is scheduling tasks, and inefficient management leads to performance decline. This paper presents a unique method for productive task scheduling with improved security in a cloud computing environment. It is suggested to design the operations using a unique Convolutional neural network optimized modified butterfly optimization (CNN-MBO) approach in order to improve efficiency and shorten construction time. Second, the data is encrypted using a hybridized RSA method, ensuring safe data transfer. The performance of our suggested technique is then evaluated by simulating it using a cloudlet simulator and analyzing the evaluation findings. The suggested method is also contrasted with alternative task scheduling-based methods for a number of performance criteria, including resource usage, reaction time, and energy consumption.

关键词： Deep learning techniques cloud security hybridized encryption privacy

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Interpretable Investigation of Feature Relevance and Sparsity of IoT Datasets 6

Interpretable Investigation of Feature Relevance and Sparsit...

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

作者： Bala, Saroj Arora, Kumud Department of MCA Ajay Kumar Garg Engineering College Ghaziabad India Department of CSE-AI/ML Indraprastha Engineering College Ghaziabad India

ISBN: (纸本)9798331522667

The rapid growth of the Internet of Things has led to the generation of vast amounts of data, which presents both challenges and opportunities for researchers and practitioners. Machine learning and deep learning techniques have emerged as powerful tools for extracting valuable insights from IoT datasets. This paper presents an empirical investigation of various ML and DL models to uncover the feature relevance in IoT datasets. The review highlights the strengths and limitations of these techniques and discusses the challenges associated with their application in the IoT domain. It explores LASSONET for feature sparsity and explainable AI techniques such as LIME, SHAP and ELI5, for feature relevance. The findings of this study can inform the development of more effective and interpretable IoT analytics solutions, ultimately enabling better decision-making and optimization of IoT systems. © 2025 IEEE.

关键词： Contrastive learning

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