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Game-Based learning System for Improvising student’s learning Effectively: A Survey 5th

Game-Based Learning System for Improvising Student’s Learni...

引用

5th international conference on Emerging Technologies in Computer engineering: Cognitive Computing and intelligent IoT, ICETCE 2022

作者： Monish, E.S. Sharma, Ankit Agarwal, Basant Jain, Sonal MNIT Campus Jaipur India Department of Computer Science and Engineering JK Lakshmipat University Jaipur India

ISBN: (纸本)9783031070112

Game-based learning approaches and 21st-century skills have been gaining a lot of attention from researchers. Given there are numerous researches and papers that support the effect of games on learning, a growing number of researchers are determined to implement educational games to develop 21st-century skills in students. This review shows how Game-Based learning techniques impact 21st Century skills. 22 recent papers have been analyzed and categorized according to learning outcome, age group, game design elements incorporated, and the type of game (Game genre) implemented. The range of game genres and game design elements as well as learning theories used in these studies are discussed. The impact of implementing machine learning strategies and techniques in educational games has also been discussed. This study contributes to the ongoing research on the use of gaming features for the development of innovative methodologies in teaching and learning. This study aims to shed light on the factors and characteristics to be considered when implementing a game-based learning system. This study provides valuable insight to future researchers and game designers with issues and problems related to educational game design and implementation. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Game design

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automated Detection and Recognition of Wild Dolphin Behaviors Using Deep learning 1

引用

1st international Artificial Intelligence conference, IAIC 2023

作者： Lin, Jiahua Gui, Duan Xie, Quan Zhou, Xundao Shan, Yunxiao The School of Artificial Intelligence Sun Yat-sen University Guangdong Zhuhai China Shenzhen Institute Sun Yat-sen University Guangdong Shenzhen China The Southern Marine Science and Engineering Guangdong Laboratory Guangdong Zhuhai China Zhuhai China Key Laboratory of MIIT for Intelligent Products Testing and Reliability Zhuhai China

ISBN: (数字)9789819712779

ISBN: (纸本)9789819712762

We presented a deep convolutional neural network approach that was able to automatically detect and recognize the behavior of wild dolphins. This study used a deep learning approach to automatically identify and analyze three visu-ally distinct behaviors (exiting behavior, wandering behavior, and entering be-havior) in dolphins. Using data directly from video recordings by both drones and handheld cameras, an action recognition model trained on this data achieved high levels of accuracy (0.99 for exiting, 0.92 for wandering, and 0.88 for entering). The method is the first to achieve visual action recognition of dol-phins behavior, opening new possibilities for using large datasets in dolphins research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Video recording

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Towards Imbalanced Large Scale Multi-label Classification with Partially Annotated Labels 21

Towards Imbalanced Large Scale Multi-label Classification wi...

引用

21st IEEE/ACIS international conference on Software engineering Research, Management and Applications, SERA 2023

作者： Zhang, Xin Song, Yuqi Zuo, Fei Zhou, Zheqing Wang, Xiaofeng University of South Carolina Columbia United States University of Central Oklahoma Oklahoma United States Ningbo Jifeng Technology Co. China

ISBN: (纸本)9798350345889

Multi-label classification is a widely encountered problem in daily life, where an instance can be associated with multiple classes. In theory, this is a supervised learning method that requires a large amount of labeling. However, annotating data is time-consuming and may be infeasible for huge labeling spaces. In addition, label imbalance can limit the performance of multi-label classifiers, especially when some labels are missing. Therefore, it is meaningful to study how to train neural networks using partial labels. In this work, we address the issue of label imbalance and investigate how to train classifiers using partial labels in large labeling spaces. First, we introduce the pseudo-labeling technique, which allows commonly adopted networks to be applied in partially labeled settings without the need for additional complex structures. Then, we propose a novel loss function that leverages statistical information from existing datasets to effectively alleviate the label imbalance problem. In addition, we design a dynamic training scheme to reduce the dimension of the labeling space and further mitigate the imbalance. Finally, we conduct extensive experiments on some publicly available multi-label datasets such as COCO, NUS-WIDE, CUB, and Open Images to demonstrate the effectiveness of the proposed approach. The results show that our approach outperforms several state-of-The-Art methods, and surprisingly, in some partial labeling settings, our approach even exceeds the methods trained with full labels. © 2023 IEEE.

关键词： learning systems

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A Complete Piping Identification Solution for Piping and Instrumentation Diagrams 25

A Complete Piping Identification Solution for Piping and Ins...

引用

25th IEEE international conferences on High Performance Computing and Communications, 9th international conference on data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Liu, Shengyong Li, Zhongtao Zhao, Shuai Yang, Lei Zhao, Fu Ge, Chuan University of Jinan School of Information Science and Engineering University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China Jinan Housing and Urban-Rural Development Bureau Jinan China The Shandong Cyberspace Affairs Commission Jinan China Shandong Xiaoya Holding Group Co. Ltd Jinan China Shandong University Jinan China

ISBN: (纸本)9798350330014

This study proposes a pipeline identification method for identifying pipelines in Piping and Instrumentation Diagrams (P&IDs) in image format. Automating this process is an important issue for the process plant industry, as image P &IDs are currently identified manually by humans. As part of research on technology for automatic conversion of image-format piping and instrumentation diagram (P&ID) into digital P&ID, this study proposes a method combining deep learning and traditional image detection to identify various types of pipelines in image format P&ID. The proposed method includes image pipeline extraction, pipeline simulation data augmentation, pipeline identification and fusion. For multiple test P &IDs, using yolov8 based on split coordinate spatial attention and augmented with simulated data, the average precision and recall are 90.5 % and 88.5 %, respectively. Combined with LSD line detection, it takes an average of 100 seconds to recognize a complete image, and the overall pipeline recognition rate can reach 95.5 %. © 2023 IEEE.

关键词： Object recognition

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Alzheimer's Disease Detection Model Based on Multimodal data Early Fusion of Medical Neuroimaging 25

Alzheimer's Disease Detection Model Based on Multimodal Data...

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作者： Wu, Shangxiao Qin, Sheng Wang, Kaiyang Yang, Su Zhang, Shiqi School of Electronic and Information Engineering Guangxi Normal University Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips Guilin China International Joint Audit Institute Nanjing Audit University Nanjing China Swansea University Department of Computer Science Swansea United Kingdom

ISBN: (纸本)9798350330014

Alzheimer's disease (AD) is a long-term neurodegenerative disease. The research in the field of AD is rapidly devel-oping, but most research methods are based on unimodal without considering the complementarity between different modes. In this work, a multimodal-based deep learning model is proposed for AD detection. Specifically, two medical neural image modes (i.e. magnetic resonance imaging and Positron emission computed to-mography) are used to fuse into a feature map. The convolutional block attention modules and convolutional neural networks are utilized to extract the features. A multilayer perceptron is used as a model classifier for the diagnostic classification of the learned AD features. To validate the model, experiments are conducted using the Alzheimer's Disease Neuroimaging Initiative dataset. Compared with existing AD diagnostic models, this work is better, and the accuracy of this model reaches 99.17%. It provides an optional solution for AD detection. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Design of Smart Home Monitoring System for Elderly Care Based on Raspberry Pi 25

Design of Smart Home Monitoring System for Elderly Care Base...

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作者： Huang, Haoran Li, Jiaying Yu, Jiabin Xiao, Binggang Mi, Hongmei Shen, Xinke College of Information Engineering China Jiliang University Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province Hangzhou China

ISBN: (纸本)9798350330014

In the current social environment, young people are getting busier, which results in them having no time to take care of the elderly at home. The elderly need to pay attention to health problems, so it is of great significance to design an intelligent home environment and monitoring system for the elderly. This paper developed a human heart rate and blood-oxygen collection terminal system based on Raspberry Pi, Alibaba Cloud IoT (Internet of Things) Platform and sensors, and had the function of monitoring the temperature and humidity in the elderly's home. The data collected from sensors are processed by the Raspberry Pi chip and sent to the IoT platform, and finally presented to the public in a visual form. The guardian can control the operation of the air conditioner in the home according to these data so that the elderly can have comfortable living conditions. © 2023 IEEE.

关键词： Internet of things

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Diabetes Prediction and Analysis based on Ensemble learning Method 3

Diabetes Prediction and Analysis based on Ensemble Learning ...

引用

3rd international conference on Electronic Information engineering and Computer Science, EIECS 2023

作者： Zheng, Yi University of Electronic Science and Technology of China School of Mathematical Sciences Chengdu China

ISBN: (纸本)9798350316674

Diabetes is a chronic lifelong disease that can cause multiple complications. Since the beginning of the 21st century, it has rapidly developed into one of the top ten causes of death in the world. However, its early symptoms are not obvious and difficult to detect. Therefore, the main goal of this article is to identify key indicators of diabetes and use an ensemble learning method to more accurately predict whether a patient has diabetes. This article selects the classic Pima-Indians diabetes data set, balances its data through three methods, and determines an effective balancing strategy. Secondly, six machine learning methods are used to rank feature importance, and more important features are obtained based on weighted ranking. Finally, a new BP-XGBoost-RF integrated learning model was proposed. The initial weights of each base model were calculated through gray correlation analysis, and the genetic algorithm was used to optimize the weights to obtain the final comprehensive model. This model is better than single models and other integrated methods in the accuracy and comprehensive score, and can more accurately and comprehensively determine whether a patient has diabetes. Similarly, the model can be extended to the prediction of other diseases and related research in other fields. © 2023 IEEE.

关键词： diabetes ensemble learning method machine learning

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Fine Multi-View Feature Selection 21

Fine Multi-View Feature Selection

引用

21st international conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2023

作者： Chen, Tingjian Deng, Zijun School of Automation Guangdong University of Technology Guangzhou510006 China

ISBN: (纸本)9798350303810

With the rapid development of computer science, high-dimensional multi-view data has presented which contains more noise and redundant information and is not conducive to multi-view learning task. It is necessary to reduce the dimension of multi-view data. Multi-view feature selection is a common method of dimensionality reduction for multi-view data, which can effectively eliminate noise and redundant information. In addition, multi-view feature selection requires further mining consistency information and complementary information of multi-view data. To solve above problems, we propose a fine multi-view feature selection method based on supervised environment. The proposed method introduces l-{2,0} -norm to achieve fine feature selection. Further, the proposed method introduces two adaptive weighting parameter learning schemes to mine the correlation information between views and categories. This paper designs an effective alternating iterative optimization algorithm. Experiments on six multi-view datasets demonstrate the effectiveness and superiority of the proposed method. © 2023 IEEE.

关键词： Multi-view feature selection Multi-view learning Supervised learning

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Engagement in Open data Workshops: The Dark Side of Remote Settings 13th

Engagement in Open Data Workshops: The Dark Side of Remote ...

引用

13th international conference on Methodologies and intelligent Systems for Technology Enhanced learning, mis4TEL 2023

作者： Antelmi, Alessia Università di Torino Torino Italy

ISBN: (纸本)9783031421334

The increasing availability of Open data gives birth to a fertile field for interested stakeholders to create value out of them;however, limited technical expertise and poor awareness are crucial barriers to their exploitation. Because of these reasons, there is an urge for learners to acquire data and information literacy competencies, which are essential for 21st-century skills, and become familiar with available Open data sources and their potential uses. To promote the dialogue around activities to boost recognition of Open data and improve users’ skills to work with them, we proposed a series of workshops to introduce Italian high school learners to searching for, authoring, and building effective communication based on Open data. This article describes an ongoing activity and details its organization, reports preliminary results on learners’ engagement, and discusses both challenges of the remote setting as well as promising learning outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Open data

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A Review of Earth or land surface use and cover Classification using the Landsat8 Remote Sensing data for Dehradun Region

A Review of Earth or land surface use and cover Classificati...

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2023 international conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Kumar, Sachin Memoria, Minakshi Kumar, Rajiv Joshi, Kapil Anandaram, Harishchander Gupta, Amit Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science and Engineering Dehradun India Centre for Computational Engineering Networking Amrita School of Artificial Intelligence Coimbatore India Graphic Era Hill University Deptt. of Cse Dehradun India

ISBN: (纸本)9798350323917

In accordance with economic and industrial development, the number of land use or land-cover or (ESU/ESC) deviations has increased tremendously with uncontrolled population growth, particularly in emerging nations in the late 20th and primary 21st periods. In the best efficient way to understand the impact of ESU/ESC and control the effects of land change, ESU/ESC change has become a major concern for ecologists and policymakers to understand how different machine learning classifiers significantly support the estimation of ESU/ESC maps from remote sensing data. Prime purpose of the study is to categorize ESU/ESC applying different machine learning algorithms on the Google Earth Engine platform, specifically using the Random Forest, Support Vector Machine, Classification and Regression Trees to compare the effectiveness of each algorithm using accuracy estimation. Future sustainable development and a knowledge of the dynamics of our changing environment will undoubtedly benefit from ESU/ESC categorization study. © 2023 IEEE.

关键词： Neural networks

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