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Research on Waxiang Dialect Recognition Based on Wav2vec2.0 Pre-Training and Transfer learning

Research on Waxiang Dialect Recognition Based on Wav2vec2.0 ...

引用

2024 international conference on computing, Robotics and System Sciences, ICRSS 2024

作者： Deng, Mixuan Tang, Pengju Gao, Yanxia Li, Huixuan Zhen, Guodong Fu, Kaifeng Wu, Xiaotian Huaihua University Huaihua China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua China

ISBN: (纸本)9798331511296

As a unique local dialect, Waxiang dialect carries rich regional culture and historical information. However, with the acceleration of modernization and the increase of population mobility, the inheritance and protection of Waxiang dialect are facing severe challenges. By constructing the 'Xuewatong' APP- Waxiang dialect digital protection platform, based on cloud storage technology, digital resource display, multimedia integration and other technologies, and providing learning resources through big data analysis of user needs, it can effectively promote the digital recording and preservation of Waxiang dialect language resources, and significantly improve the dissemination and educational effect of Waxiang dialect culture, showing the great potential of digital technology in dialect protection. © 2024 IEEE.

关键词： Linguistics

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DDoS Attack Forecasting Using Transformers

DDoS Attack Forecasting Using Transformers

引用

2023 IEEE international conference on Dependable, Autonomic and Secure computing, 2023 international conference on Pervasive Intelligence and computing, 2023 international conference on Cloud and Big Data computing, 2023 international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Alrahmani, Zain Ahmed Elleithy, Khaled University of Bridgeport Department of Computer Science and Engineering Bridgeport United States

ISBN: (纸本)9798350304602

Distributed Denial of Service DDoS attack has been critical for many years. Many organizations have suffered from this type of attack, which, in some incidents, resulted in severe damage. Not only does this attack affect the targeted organizations, but it also impacts legitimate users of those organizations. Likely, technological advancement helps detect and prevent such risks to some extent, even though attackers always find new ways to harm their victims. The last decade has witnessed a dramatic increase in machine learning methods development. Some of the methods have been studied extensively in the area of cyber-attack. One of the technological advancements in machine learning is Transformer models. In this paper, two Transformer models are evaluated in forecasting DDoS attacks. The goal is to test these models and determine which is more suitable to prevent future attacks. The two models are Frequency Enhanced Decomposed Transformer (FEDformer) and Patch Time Series Transformer (PatchTST). The results show that PatchTST is better at forecasting DDoS attacks when compared to FEDformer, even though there is still room for improvement. © 2023 IEEE.

关键词： Forecasting

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Dino Math Legacy: A Configurable Educational Video Game for Personalized Math learning in Peruvian High Schools 31

Dino Math Legacy: A Configurable Educational Video Game for ...

引用

31st IEEE international conference on Electronics, Electrical engineering and computing, INTERCON 2024

作者： Lozano, G. Baldeon, J. Velarde, M. Navarro, R. Pontificia Universidad Católica del Perú Avatar Group Lima Peru

ISBN: (纸本)9798350378344

The use of educational video games has gained increasing popularity in recent years as a tool to enhance learning outcomes. However, there are still challenges in the way math is taught through video games, such as insufficient adaptation to diverse learning styles and limited flexibility in topic selection. These limitations have created a need for configurable video games that can be tailored to meet specific learning objectives while engaging students in an enjoyable and interactive manner. In response to the demand for more adaptable educational tools, this study introduces 'Dino Math Legacy', a customizable video game designed to reinforce math learning for first-year high school students, aligned with Peru's National Basic Education Curriculum. Teachers have the ability to tailor the game to specific learning goals, ensuring engagement through interactive and enjoyable experiences. This study aimed to evaluate the effectiveness of a video game in improving math learning for first-year high school students, focusing on its impact on engagement and learning outcomes. A mixed-methods evaluation confirmed the game's positive impact on learning outcomes and its successful integration of Human-Computer Interaction (HCI) principles, which enhance user experience and learning. Additionally, the study found that the game improved math learning by incorporating dynamic and interactive features, such as adaptive problem-solving challenges, tailored to individual learning styles. The game's customizable features allowed teachers to modify content and set specific learning objectives, which enhanced student motivation and improved comprehension of key math concepts. The research highlights the potential of educational video games in classroom learning, emphasizing the importance of aligning design with student learning styles. A user-centered design approach, guided by educational psychologists and frameworks like LEGA and LM-GM, ensured effectiveness. A comprehensive mixe

关键词： Students

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learning from Time-Tracking Data: Gaussian Process Regression for Efficient Time Management Systems

Learning from Time-Tracking Data: Gaussian Process Regressio...

引用

2024 IEEE international conference on Communication, computing and Signal Processing, IICCCS 2024

作者： Thakur, Bandna Allirani, P. Chourasiya, Prashant Srinu, K. Al-Farouni, Mohammed Santhosh, S. Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Aditya Engineering College Department of Artificial Intelligence & Machine Learning Surampalem India The Islamic University Department Of Computers Techniques Engineering College Of Technical Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Department of Ise Nitte India

ISBN: (纸本)9798350390759

This paper aims at analyzing the use of Gaussian Process Regression (GPR) in working and creating effective time management system. It incorporates the GPR model that helps in predicting the future time usage by dissecting the time-study data this assists the users in scheduling. The approach adopted entails harvesting data from automated tools and manual logs, pre-processing the data collected, using k-fold cross-validation in training and validating the model. The forecasting accuracy features a low Mean Absolute Error and a high R-squared value, which signifies that the models perform well in terms of predictive capability. This research proves that GPR can actively help with data on time management and benefit time management systems as a whole. Some of the challenges that faced the research include quality of the data and the computation required to process the data was enhanced by effective preprocessing and refined algorithms respectively. © 2024 IEEE.

关键词： Data accuracy

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Gamification: An Emerging Educational Technology for Student learning 2

Gamification: An Emerging Educational Technology for Student...

引用

2nd international conference on Recent Trends in Microelectronics, Automation, computing, and Communications Systems, ICMACC 2024

作者： Banerjee, Subeer Banerjee, Richa Gulati, Chanda Chauhan, Abhijeet Singh Sriram Institute of Information Technology M.P. Gwalior India Narsee Monjee Institute of Management Studies Maharashtra Mumbai India Prestige Institute of Management and Research Department of Management Gwalior India

ISBN: (纸本)9798350366570

Gamification in education is an excellent technique to make students more engaged and motivated in their studies. By incorporating games into the learning process, students can have fun while they learn and develop new skills. There are many ways to gamify education, and each classroom can find the right approach for them. Gamification has become a potential strategy to improve educational environments across the board. This study looks into how motivation and engagement affect learning outcomes when gamification is incorporated into the teaching and learning process. The present study focused on students of higher education engaged in learning language from a game. It analyzed the relationship between student engagement, Motivation for learning, skills for learning, and learning through gamification with the help of data that was collected on a standardized questionnaire from 256 students. The study evaluates how different gamification components, such as points, badges, and leader-boards, affect student motivation and engagement levels. The impact of motivation and engagement was evident on the learning of students. The results suggested that all independent variables have a strong affirmative relationship with learning through gamification as the dependent variable. By highlighting the crucial roles motivation and engagement play in improving learning outcomes, the study report enhances the increasing body of familiarity with the incorporation of gamification in education. For educators, instructional designers, and policymakers interested in using the potential of gamification to develop more efficient and interesting learning environments in the twenty-first century, the findings have practical ramifications. © 2024 IEEE.

关键词： Federated learning

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Study on Self learning Systems for Autonomous Soft Landing of Spacecrafts on Lunar Surfaces

Study on Self Learning Systems for Autonomous Soft Landing o...

引用

2024 international conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Suganthi, Su Ganesan, J. Jeya Shanthi, P.A. Anirudh, U.G. Swathii, C.P. Sri Sai Ram Institute Of Technology Chennai India Sri Sai Ram Engineering College Chennai India

ISBN: (纸本)9798350352689

With the rising interest in space exploration there is a rapid development of spacecraft technology. One of the biggest issues faced in space exploration is the soft landing of the exploration devices, due to its complex and stochastic nature. This study aims to propose a solution which solves the problem with the help of a self-learning, single-agent system powered by Reinforcement learning. The Reinforcement learning agent is made to act out in a simulation comprising of a realistic 2D model of the lunar surface and the lunar lander. A comprehensive comparison is conducted between the Proximal Policy Optimization, Deep Q Network, Advantage actor-critic algorithms to verify the best performing algorithm. The problem is approached as a combination of supervised and unsupervised learning techniques. The unsupervised learning component is used to alter the lander's path based on its own experience whereas the supervised learning component directs the lander to follow a planned course toward the lunar surface. This paves way for the automation of soft moon landing while minimizing human intervention, greatly increasing the probability of success of the mission. After thorough experimentation it is found that Proximal Policy Optimization performs the best with the highest mean rewards and lowest standard deviation rewards respectively. © 2024 IEEE.

关键词： Unsupervised learning

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A Novel Inception Deep learning model for Mobile AI Stroke Prediction system: AI Research and Industry Perspectives for Connected Health 6

A Novel Inception Deep Learning model for Mobile AI Stroke P...

引用

6th international conference on computing and Informatics, ICCI 2024

作者： Elbagoury, Bassant M. Maskeliuans, Rytis Zaghow, Marwa Kamel, Nabil Faculty of Computer and Information Sciences Ain Shams University Computer Science and Artificial Intelligence Programs Egypt Faculty of Computer Science and Engineering King Salman International University KSIU Egypt Kaunas University of Technology Faculty of Informatics Department of Multimedia Engineering Lithuania Dell Technologies EMEA Egypt

ISBN: (纸本)9798350373875

Artificial intelligence technologies for the mobile health and smart hospitals are important, by applying predictive Analytics and Deep learning algorithms and developing new models. The main objective is applying artificial Intelligence methods in the medical field, especially for heart/brain stroke diseases diagnosis and prediction for emergency patients' cases. Thus, to save patients' lives, also through the integration with IoT and wearable technologies, which are integrated with AI and DL algorithms that help to make sense of bio signals predictive analytics such as biomedical sensors processing. and complex diseases predictions and early detection like heart and stroke diseases. Also dealing with EMG sensors for stroke prediction. Moreover, Intelligent Mobile Health based on our previously introduced project of AI smart hospital and Mobile AI stroke prediction system for connected health and stroke emergencies. In this paper, a new deep learning model has been built and tested for Stroke EMG signal prediction by modifying the Inception-v3 architecture and other deep learning models. Also, this paper compares current results of Mobile AI health Inception model with our previous developed Mobile AI stroke engine that depends on hybrid LSTM deep learning for EMG signals prediction. Both models have achieved high accuracies. Moreover, the inception model is more stable and higher average accuracies that reaches 98%. Moreover, AI research and future industry implementations for Generative AI Dell Technologies servers is discussed. © 2024 IEEE.

关键词： learning systems

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A Machine learning Approach for Reliable Object Tracking 1

引用

UNIfied conference of international Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, international conference on Maintenance engineering, IncoME-V 2023, international conference on the Efficiency and Performance engineering Network, TEPEN 2023

作者： Amjad, Bisma Ahmed, Qasim Z. Lazaridis, Pavlos Khan, Faheem Hafeez, Maryam Zaharis, Zaharias D. School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom School of Electrical and Computing Engineering Aristotle University of Thessaloniki Thessaloniki541 24 Greece

ISBN: (数字)9783031494130

ISBN: (纸本)9783031494123

Object tracking is a critical task that finds its applications in various fields including surveillance and autonomous robots. However, most of the work on object tracking has been developed on images and video data. In contrast, the aim of our work is to develop reliable object tracking system based on sequence of measurements which can be obtained from radio sensors, that are more suitable for privacy-concerned applications. In addition, we propose to use linear regression, in contrast to complex data-driven models, to demonstrate its performance against conventional tracking algorithm i.e., particle filter. Our experimental results show that LR can predict a moving object’s position with minimal error and significantly outperforms the particle filter by more than 90%. All the experiments have been validated via simulations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Machine learning

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Evaluation of the New Electrical engineering Program Qualification Mix (PQM) in an Open Distance learning (ODeL) Environment

Evaluation of the New Electrical Engineering Program Qualifi...

引用

2023 IEEE international conference on Industrial engineering and engineering Management, IEEM 2023

作者： Hlalele, T.S. University of South Africa Department of Electrical Engineering Johannesburg South Africa

ISBN: (纸本)9798350323153

The ODeL education has emerged to be the fastest growing method of teaching in the world today. In South Africa, it has been adopted by several universities and the universities of technology. The University of South Africa (UNISA) is the largest full accredited institution of higher learning to offer ODeL in South Africa. The new programme qualification mixture (PQM) in engineering, which has been implemented requires change in teaching methods, study materials and improved virtual learning environments. The engineering Council of South Africa (ECSA) standards and requirements need to be met by this new curriculum. This paper evaluates the new PQM based on ECSA requirements. A comparison is made between the module of the same content in the new PQM and the old PQM. We conclude by paying attention to new approaches to be utilized and further identify the challenges and possible solutions to this new PQM. © 2023 IEEE.

关键词： new curricula OdeL education Programme qualification mix

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Research on Information Fusion Method of English teaching Reform Based on Reinforcement learning 9th

Research on Information Fusion Method of English Teaching Re...

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9th EAI international conference on e-learning, e-Education, and Online Training, eLEOT 2023

作者： Dong, Hui Zhao, Dan Medical School of Jinshan Branch Affiliated to Shanghai University of Medicine & Health Sciences Shanghai200237 China Department of Public Foundation Courses Wuhan Institute of Design and Sciences Wuhan430000 China

ISBN: (纸本)9783031514708

The information of English teaching reform has such attributes as different time and space, heterogeneous form, etc., which brings difficulties to multi-source data fusion and decision-making. The existing multimodal models may have some missing modal data or conflicts between modalities, which will bring difficulties to information fusion in English teaching reform. Therefore, an information fusion method of English teaching reform based on reinforcement learning is designed. The clustering algorithm based on multi-agent Reinforcement learning is adopted to implement the information mining of English teaching reform. Implement data cleaning for mining information, including missing data filling and duplicate data cleaning. The dynamic invariant special representation fusion network is designed, which is composed of the improved joint embedded domain separation network and the fusion representation to realize the information fusion of English teaching reform. The test results show that the average absolute error of the design method in fusion is lower than 0.700% as a whole, and the Pearson correlation coefficient is higher than 0.4 as a whole. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Correlation methods

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