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machine-learning-Based Software to Group Heterogeneous Students for Online Peer Assessment Activities 4th

Machine-Learning-Based Software to Group Heterogeneous Stude...

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4th international conference on Higher Education learning Methodologies and Technologies Online, HELMeTO 2022

作者： Amendola, Daniela Nalli, Giacomo Miceli, Cristina University of Camerino Via Andrea D’Accorso 16 Camerino62032 Italy

ISBN: (纸本)9783031297991

Since the academic year 2017/2018, a peer assessment activity was included in the online Genomics laboratory for the master’s degree course in Biological Sciences of the University of Camerino, with the aim of improving learning outcomes and soft skills in students, such as team building and critical thinking. Creating groups in university courses is not easy because of the large number of students, that leads teachers to realize groups totally randomly, a procedure that is not always effective. One of the factors that influences the success of collaborative learning is the creation of heterogeneous groups based on the students’ behaviors. Despite little improvements, the online genomics laboratory highlighted some gaps. Random groups didn’t ensure that each group was composed of heterogeneous students, and it leads some students to have a bad perception of the peer review activity, negatively affecting their engagement and motivation. this work proposes a new machine learning Approach and the realization of a specific software, able to create effective heterogeneous groups to be involved in the online peer assessment process, in order to improve learning outcomes and satisfaction in the students. the aim is to check the improvement of the peer assessment effectiveness using heterogeneous groups compared to random groups of students. Two editions of the online laboratory of Genomics were analysed, examining the students’ results and perceptions to verify the impact of the machine learning approach designed in this work. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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An Intelligent Screening Mobile application for Alzheimer's Disease Using Clock Drawing Test 2021

An Intelligent Screening Mobile Application for Alzheimer's ...

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4th international conference on Signal Processing and machine learning, SPML 2021

作者： Qian, Chen Liao, Minyuan School of Computer Science and Technology Donghua University Shanghai China

ISBN: (纸本)9781450390170

Clock drawing test is a classic and reliable clinical assessment of cognitive functions. However, it requires manual inspection and scoring. In this paper, via several computer vision and artificial intelligence techniques, we successfully design and implement an intelligent system with the ability to score the test in an automatic manner. As a mobile application, our work allows users to take a clock drawing test anytime and anywhere. © 2021 ACM.

关键词： Clocks

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Construction and application of Talent Management Cockpit in Aerospace Enterprises 23

Construction and Application of Talent Management Cockpit in...

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proceedings of the 2023 4th international conference on computer Science and Management Technology

作者： Qingqing Zhao Xuedong Zhang Huili Fan Qixian Liu Lanyu Gao Wenwei Gao Beijing Institute of Space Mechanics & Electricity China

ISBN: (纸本)9798400709517

With the continuous development of information technology in human resource management, it is particularly important to explore the explicit value of existing personnel information in the human resource system. this paper presents the construction method and application of talent management cockpit in aerospace enterprises. Firstly, this paper constructs a set of talent management index systems specially designed for the aerospace industry. Secondly, combining the statistical indicators commonly used in human resource systems and reports, this paper puts forward an index model based on core elements. Finally, this paper realizes a "1+3+1" talent management cockpit. the cockpit combines the main dashboard, sub-dashboard, and theme dashboard, which are systematically implemented using visual reporting tools. Users can intuitively and conveniently understand the real-time distribution and change of the enterprise's internal personnel. Business managers can make more accurate decisions and promote business development.

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Research on the application of eSIM in New Power Systems 23

Research on the Application of eSIM in New Power Systems

引用

proceedings of the 2023 4th international conference on computer Science and Management Technology

作者： Jianxin Chu Bao Li Hongyi Zhou Xiang Li State Grid Zhejiang Haiyan County Power Supply Co. Ltd China

ISBN: (纸本)9798400709517

With the rapid advancement of information and communication technology, the distinctive technical advantages of eSIM in IoT communications have become increasingly evident, capturing significant attention within the industry. However, power grid enterprises currently face several constraints, including security and reliability, which continue to pose substantial challenges for the widespread adoption of eSIM. In light of the specific demands of the power grid industry, this article comprehensively examines the current status and application potential of eSIM. Building upon this analysis, the study investigates key aspects such as management platform construction, network selection strategies, security technologies, and intelligent applications, aiming to devise targeted strategies and propose research objectives. Drawing upon the research findings, an eSIM management platform incorporating SM-DP functionality is developed to enable remote network switching, thereby offering valuable insights and guidance for large-scale industry implementations.

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Comparison and Simulation of Deep learning Detection Algorithms for Floating Objects on the Water Surface

Comparison and Simulation of Deep Learning Detection Algorit...

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international conference on computer Engineering and applications (ICCEA)

作者： Zhengpeng Pu Xianshuang Geng Dong Sun Huiyun Feng Jie Chen Jingang Jiang School of Electronic and Information Engineering Anhui Jianzhu University Hefei China Power China Eco-Environmental Group CO. LTD Shenzhen China Chinese Academy of Sciences Institute of Intelligent Machines Hefei Institutes of Physical Science Hefei China

Cleaning up floating waste on the river surface is an important means of preventing ecological pollution and slowing down the growth of marine waste. Unmanned boats combined with real-time object detection algorithms can provide solutions for cleaning floating garbage from rivers. However, the detection accuracy and response speed of different object detection algorithms vary greatly when dealing with different complex scenarios. the study uses five classical object detection algorithms: Faster RCNN, Cascade RCNN, Yolo v3, Yolo v5-s, Yolo v5-m, and FloW-Img, a river floating trash dataset, to experimentally verify and compare the detection accuracy and efficiency of each algorithm. the experimental results show that, in terms of detection accuracy, the Yolo v3 model has higher detection accuracy than the remaining four algorithm models, with mAP@[IoU=0.5] of 0.924 and mAP@[IoU=0.5:0.95] of 0.536; and in terms of model parameter scale, the Yolo v5-s model has the smallest parameter scale of 13.36Mbit; in terms of detection rate, the Yolo v5-s model has the fastest detection rate, with FPS of 78.13 in the server environment and 31.3 in the embedded simulation environment. the results of the above experimental indexes show that the Yolo v3 algorithm model is suitable for inland floating object detection scenarios with high accuracy requirements; Yolo v5-s and Yolo v5-m have smaller parameter scales and are more suitable for embedded device detection applications.

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Crime Alert through Smart Surveillance Using Deep learning Techniques 22

Crime Alert Through Smart Surveillance Using Deep Learning T...

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proceedings of the 4th international conference on Information Management & machine Intelligence

作者： Aarushi Dua Bhavya Kalra Abhishek Bhatia Mauktik Madan Anuradha Dhull Yogita Gigras Computer Science Department The NorthCap University India

ISBN: (纸本)9781450399937

In this paper, we proposed a way to detect different categories of abnormal activities happening in India such as fight, explosion and shootout. the goal is to build a model that can identify signs of violence and aggression in videos and separates out anomalies from normal patterns. the problem statement of crime detection is achieved in two broad steps: One by building a deep learning model (ConvLSTM) to categorize different crimes and then deploying this model to an interface where live stream footage is connected to the server for any crime alert. the model is evaluated on UCF-crime dataset and if any frame in live stream captures a criminal act, the tool will issue a detection warning for a danger situation, signaling suspicious actions at a certain point in time. the suggested ConvLSTM2D approach outperforms the traditional convolutional neural networks-long short-term memory (CNN-LSTM) algorithms in terms of accuracy. the performance evaluation of training data is based on Area under the Receiver Operator Characteristic (ROC) curve.

关键词： Crime Anomaly LSTM Conv3D ConvLSTM Smart Surveillance AUC UCF crime

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Supporting Instructors with Course Attendance and Quality Prediction in Synchronous learning 4th

Supporting Instructors with Course Attendance and Quality ...

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4th international conference on Higher Education learning Methodologies and Technologies Online, HELMeTO 2022

作者： Fenu, Gianni Galici, Roberta Marras, Mirko Picciau, Simone Department of Mathematics and Computer Science University of Cagliari Cagliari Italy

ISBN: (纸本)9783031297991

the massive adoption of artificial intelligence has opened up the opportunity for a range of intelligent technologies that can support education. Empowering instructors with tools able to early predict the attendance and quality of their courses and consequently make prompt adjustments is one of them. However, the potential of these tools has by no means been researched exhaustively within synchronous courses, with prior work mostly focused on courses based on pre-recorded lectures. In this paper, we examine the predictive power of machine-learning models on the future attendance of students to synchronous courses and their perceived quality. To this end, we leverage both (i) attendance records to online real-time lectures within courses of a large public university, and (ii) responses to course quality questionnaires completed at the end of each course. Experiments show that our models can accurately and early predict attendance to courses and key aspects characterizing course quality (e.g., study workload). Our findings confirm the potential of intelligent models to support instructors in managing and promptly reacting within their courses, to increase engagement and reduce dropout. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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application of a Data Collection Method Using Cloud Services in Smart Home 23

Application of a Data Collection Method Using Cloud Services...

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proceedings of the 2023 4th international conference on Big Data Economy and Information Management

作者： Xiaoquan You Qinchun Hu Lifang Guo Jianglong Yuan School of Network and Communication Engineering Chengdu Technological University China School of Automation and Electrical Engineering Chengdu Technological University China School of Communications and Information Engineering Chongqing University of Posts and Telecommunications China

ISBN: (纸本)9798400716669

In order to meet people's demand for smart homes, we propose the design and implementation of a smart home system based on embedded ARM architecture. the data acquisition subsystem and equipment control subsystem of the system are implemented with STM32 and Raspberry Pi 3B+ as the core, respectively. Wireless data communication is achieved through Wi-Fi, and redundant NB-IOT wireless connections are included to enhance system reliability. the system's monitoring data is stored in the cloud platform, allowing for user behavior analysis and intelligent home control. System test results confirm the system's stable operation, user-friendly human-computer interaction, and excellent scalability. It is particularly suitable for the intelligent transformation of homes in older residential areas, and it has a promising market application outlook.

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Gaussian process multi-label classification based on multi-view similarity fusion 22

Gaussian process multi-label classification based on multi-v...

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proceedings of the 2022 4th international conference on Robotics, Intelligent Control and Artificial Intelligence

作者： Hui Xiang Shenming Gu School of Information Engineering Zhejiang Ocean University China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University China

ISBN: (纸本)9781450398343

Multi-view multi-label learning serves as a vital framework for learning from objects with diverse representations and rich semantics. the Gaussian process (GP), as an efficient and flexible Bayesian nonparametric model, the GP has effectively solved various tasks in machine learning. However, the Gaussian process has rarely been directly applied to multi-view multi-label learning scenarios. therefore, in this paper, we propose a new multi-view multi-label learning method (mvml-DSimGP) based on the Gaussian pass latent variable model (GPLVM), which consists of similar non-parametric mapping function learning, rule constraint learning and label learning. Similarity nonparametric study mapping function for multiple-views data analysis, it studied the view within the similarity and latent mapping function between the parameters. On the basis of the similarity of nonparametric function learning, regular constraints are introduced, and put forward multi-label learning. Instances with similar label information are encouraged to have similar output representations in the embedded space, making it sufficient discernment in the embedded space. Numerous experiments on real-world datasets clearly show that mvml-DSimGP has good performance compared to other general multi-view multi-label learning methods.

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GraphicalAI: A user-centric approach to develop artificial intelligence and machine learning applications using a visual and graphical language 21

GraphicalAI: A user-centric approach to develop artificial i...

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4th international conference on Data Storage and Data Engineering, DSDE 2021

作者： Shen, Andrew Sun, Yu Computer Science Department California State Polytechnic University PomonaCA91768 United States

ISBN: (纸本)9781450389303

With the increasing popularity of Artificial Intelligence (AI) and machine learning (ML), developing AI-based applications is in high demand in various industries. However, the AI development is still based on traditional programming frameworks and languages, which prevents domain experts from contributing to it without collaborating with developers. this research is to show how graphical software allows users from many domain (e.g., Doctors, Accountants, Advertisers) to build AI applications, train AI models without any prior knowledge of programming, and many of its unnecessary concepts. Using nodes and connectors as the primary graphical components, the application, GraphicalAI, is to show how graphics can be designed in a way to easily prototype any kinds of AI models. To enable domain experts to design AI models using the power of graphics and our human vision. © 2021 ACM.

关键词： Visual languages

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