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Digital course scheduling system for data-driven industrial worker training based on 5G enabling environment

Digital course scheduling system for data-driven industrial ...

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2024 international conference on Artificial Intelligence, Deep Learning and Neural Networks, AIDLNN 2024

作者： Chen, Yuzhong Zhong, Yue Guangdong Construction Polytechnic Guangdong511500 China

ISBN: (纸本)9798331520816

5G wisdom on the basis of digital campus, campus under the effective use of 5G technology of Internet of things, cloud computing, big data and other advanced information engineering to carry out a full range of campus construction, promoting the scientific decision-making, build open teaching environment, good for all the teachers and students to provide more convenient and comfortable place to live, work, study[1]. this article mainly around college students online course management system implementation technology selection, system requirements, database design and so on carries on the analysis, thus the college students' course selection management system based on B/S structure, B/S architecture to implement development, convenient maintenance, and to implement low cost price, so as to achieve high efficiency low cost development, the main functional modules of the system are: Student and teacher information management, user rights control, course management, classroom management, class management, course selection management, class withdrawal, course selection statistics management, students, teachers and school administrators only need to click on the browser to access the system can carry out the corresponding course selection and system management and maintenance, Java programming language and popular frameworks such as Spring Boot, Bootstrap and some Ajax technologies are selected to achieve the smoothness of data interaction at the front and back end of the system. With the advent of the Internet era, the birth of Internet +[2], which can be added to a large number, gradually adds every bit of life, there is no doubt that education has always been a hot issue in the world. © 2024 IEEE.

关键词： Java programming language

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Method for identifying OPGW cable vibration events based on optimized imbalanced data 5

Method for identifying OPGW cable vibration events based on ...

引用

5th international conference on Computer Vision and data Mining, ICCVDM 2024

作者： Chen, Xiaojuan Li, Haodong Li, Bo Liu, Chen School of Electronic Information Engineering Changchun University of Science and Technology Jilin Changchun130013 China Electric Power Science Research Institute State Grid Eastern Inner Mongolia Electric Power Co Neimenghgu Huhehaote010020 China

ISBN: (纸本)9781510682863

Fiber optic composite overhead ground wire (OPGW) bears the important task of transmitting power data and guaranteeing the safety of the power grid, but OPGW cables in the process of operation will often be due to bird pecking, wind dance, ice and other anomalies that affect the normal operation of the cable. therefore, the identification of abnormal events affecting the operation of fiber optic cables is crucial to the operation of the power system. However, due to geographic and environmental factors, the collected cable anomaly data is often unbalanced, which affects the performance of the classifier and leads to misclassification. Aiming at such problems, this paper proposes a data optimization method that combines improved K-means and Borderline-SMOTE. After calculating the data density and Euclidean distance of the fiber optic cable vibration data collected by phase-sensitive optical time-domain reflectometer (φ-OTDR), the data is divided into clusters by using the improved K-means algorithm, and the samples of a few classes that are easy to be misclassified at the decision boundary of each cluster are identified, and the samples of such classes are classified by using the improved K-means algorithm. A few classes of samples are identified at the decision boundary in each cluster, and such samples are oversampled using the Borderline-SMOTE method to balance the data volume of various types of anomalous events. the experimental results show that the accuracy, recall and F1 score of the three classes of machine learning algorithms combined with this method are improved by more than 5% compared with the traditional methods. © 2024 SPIE.

关键词： Reflectometers

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Building A Student Knowledge Based Ontology Approach Using Web Ontology Language 5

Building A Student Knowledge Based Ontology Approach Using W...

引用

5th IEEE international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Reddy, A. Brahmananda Akhil, Karnam Anuradha, Alampally Shriya, Golla Bainee, Kumkum Prasad, R. Sai Giridhar Vnr Vignana Jyothi Institute Of Engineering And Technology Department Of Computer Science Engineering Hyderabad India

ISBN: (纸本)9798350330861

Education is rapidly progressing and it is very important to have a bridge between innovative skills and conceptual learning. Education will provide learning abilities through various modes in sense of cognitive abilities and applying knowledge to gain optimality. Apart from these skills it is very important to have good behaviour to enhance the quality of life. Currently children are facing a lot of issues with academics, emotional imbalance, anxiety and depression, mental illness and lack of physical fitness. To overcome the above issues a knowledge-based ontology approach is presented and proposed using Web Ontology Language (OWL). To achieve this, survey was conducted among students of age group 1823 to analyse their SWOT analysis, emotional stability of a student, intelligent quotient by understanding their personas, habitat in schooling, intermediate, graduation, sports and curricular activities. Surveys were circulated through google forms, face to face interaction were conducted and responses were noted. Based on the responses from the survey analysis, entities and objects are identified and relationships between them were inter linked. Ontologies are predominantly used to identify the relations between entities and objects, using OWL on platform protégé editor, for the identified entities relations were build. the generated output which are in the form of graphs are termed as knowledge-base are stored in the inference engine. SWRL was used to validate the proposed ontology to provide the optimal results. © 2023 IEEE.

关键词： Ontology

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Integrating Machine Learning Models for Chili Disease Prediction and Economic Impact Analysis 5

Integrating Machine Learning Models for Chili Disease Predic...

引用

5th international conference on Advancements in Computing, ICAC 2023

作者： Madhusankha, N.H.D. Gamage, S.K. Koonara, K.M.N.D. Chathurika, Bhagyanie Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798350358131

In this research, we seamlessly integrate machine learning methodologies into chili farming practices within tunnel environments, addressing the dual challenges of disease detection and market trend interpretation. Our study employs an image processing algorithm enhanced with machine learning techniques, achieving high accuracy in identifying and diagnosing prevalent chili crop diseases in Sri Lanka. With an impressive F1 score of 0.9444 and a mean absolute error of 0.1489, a training accuracy of 98.58% our disease prediction model provides practical mitigation strategies to farmers, ensuring minimal losses. 50 instances were separated from each class for testing purposes. 80% of the remaining data was utilized for training, while the remaining 20% was allocated for validation. Simultaneously, we leverage historical market data and price trend analysis through a mobile application, enabling real-time prediction of the most profitable chili crop varieties for cultivation. However, our findings highlight challenges in market trend analysis, reflected in our training MAE of 7.57 and RMSE of 16.54. Although our disease prediction model excels, the integration of both concepts remains a challenge. Despite this, our research lays the foundation for transformative impacts on chili farming, propelling productivity, minimizing losses, and fostering sustainable growth in the industry. © 2023 IEEE.

关键词： Machine learning

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Application of Facial Emotion Recognition Based on Multi-Scale Fusion in Smarter Education 5

Application of Facial Emotion Recognition Based on Multi-Sca...

引用

5th international conference on Computer Science and Technologies in Education, CSTE 2023

作者： Jiao, Ge Wang, Fangyan Li, Lang Zheng, Guangyong College of Computer Science and Technology Hengyang Normal University Hengyang China

ISBN: (纸本)9798350325720

the traditional teaching mode has little interaction between teachers and students, and teachers can not grasp students' emotional changes in time. therefore, how to timely and effectively feed back students' emotional state to teachers in class has become an urgent problem to be solved in current classroom teaching. this paper takes the classroom as the application scenario and uses the deep learning technology to propose a residual attention network and multi-scale fusion student emotion recognition model. the model uses image preprocessing to improve the quality of the input image, uses residual attention module to guide the network to focus on local salient features, uses multi-scale fusion module to strengthen the information interaction between different levels and expand the receptive field, and obtains high accuracy on FER2013 data set. Using this method to identify the students' attention state in class and judge the teacher's teaching effect and students' classroom listening attitude can correct students' listening attitude, improve teachers' teaching efficiency and promote the improvement of teaching quality. © 2023 IEEE.

关键词： Emotion Recognition

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An Energy-Efficient D2D Communications Overlaying Cellular Networks 7

An Energy-Efficient D2D Communications Overlaying Cellular N...

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7th international conference on Electronic Information technology and Computer engineering (EITCE)

作者： Yu, Yang Liu, Xin Ge, Yinchao Chen, Jia Hubei Three Gorges Polytech Elect Informat Sch Yichang Peoples R China Wuhan Qingchuan Univ Inst Elect Informat Wuhan Hubei Peoples R China Second Ship Design & Res Inst Wuhan Res Lab 5 Wuhan Peoples R China

ISBN: (纸本)9798400708305

Device-to-device (D2D) communication, widely used in the 5G era, supplements cellular networks. It improves network throughput and reduces cellular data service load. In response to the concept of green communication, wireless resource allocation needs to improve energy efficiency (EE) as much as possible. Due to the limited battery capacity of mobile terminals, a resource-scheduling scheme is proposed, which involves joint resource allocation and link-matching according to the minimum power consumption criterion. Newton's method solves the resource allocation problem via a few iterations. then, we formulate the matching between cellular users (CUs) and D2D pairs as a weighted bipartite matching problem, which is tackled via the Kuhn-Munkres algorithm. We verify the benefits of the proposed scheme through simulation.

关键词： D2D communication green communication resource allocation link matching Kuhn-Munkres algorithm

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Early Detection of Kidney Disease Risk Factors through IoT-Enabled Machine Learning Systems 5th

Early Detection of Kidney Disease Risk Factors Through IoT-E...

引用

5th international conference on data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Ravi, M. Krishnaiah, Pokala Dileep, Chilukuri Annapoorna, B. Reddy, M. Janga Satyanarayana, B. CMR Institute of Technology Telangana Hyderabad501401 India

ISBN: (纸本)9789819780426

the combination of IoT and ML revolutionizes healthcare by enabling early kidney disease detection, redefining proactive patient care with precision and efficiency. Leveraging real-time data from IoT-enabled devices, ML algorithms analyze vital parameters, facilitating the identification of subtle risk factors. Personalized risk models, considering genetics and lifestyle, empower timely interventions. this innovative approach fosters continuous patient-healthcare provider communication, supporting collaborative, patient-centric care. the convergence of IoT and ML, underpinned by robust security measures, establishes a dynamic healthcare ecosystem. this proactive, data-driven model presents a significant stride toward precision medicine and personalized healthcare solutions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Identifying Acute Disseminated Encephalomyelitis with Time Series Analysis 5th

Identifying Acute Disseminated Encephalomyelitis with Time S...

引用

5th international conference on data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Pallavi, M. Mishra, Awakash Kudari, Jayashree M. Belwal, Ankit School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

Acute disseminated encephalomyelitis (ADEM) is an extraordinary immune-mediated disease that influences the mind and spinal cord, central to the unexpected onset of neurologic symptoms. Time series evaluation can be used to become aware of ADEM in patients. Mainly, statistical analysis of the temporal styles of mind imaging and immunological parameters has proven effective for diagnosing ADEM in medical settings. Such material styles offer essential clues about disease development, which can be leveraged to diagnose ADEM patients appropriately. Acute disseminated encephalomyelitis (ADEM) is a critical degenerative sickness of the imperative nervous device, characterized by a rapid onset of neurologic signs and significant infection of the mind and spinal twine. Early analysis of ADEM is critical to initiate appropriate remedies and reduce long-term incapacity. In this look, we advocate the usage of time collection analysis to discover neurological patterns related to ADEM from the brain indicators of sufferers. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Time series

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Perspectives on industry 4.0 awareness among undergraduate IT students in IRAQ: University of Anbar as a case study. 16

Perspectives on industry 4.0 awareness among undergraduate I...

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16th international conference on Developments in eSystems engineering, DeSE 2023

作者： Aljaaf, Ahmed J. Ibrahim, Mohammed Al-Ouqaili, Mushtak T. S. Neda Al-Jumeily, Dhiya Mustafina, Jamila Al-Rawi, Aysha Rasheed, Maha M. University of Anbar Anbar Ramadi Iraq Ljmu Faculty of Engineering and Technology Liverpool United Kingdom

ISBN: (纸本)9798350381344

the recent era of technology, Industry 4.0 finds its way to the field of manufacturing based on advanced technology such as artificial intelligence, internet of things, augmented reality, robotics, and more. these technologies can truly lead a massive transformation in production and manufacturing processes. this study aims to understand the extent to which undergraduate students in the field computer science and information technology in Iraq are aware of the techniques and applications of Industry 4.0. For this purpose, we have reviewed the literature as well as have collected opinions of students using a paper-based survey at the college of computer science and IT, University of Anbar. this can help in determining the level of students' awareness of Industry 4.0 and to identify limitations, if any, when it comes to their educational journey. data has been analyzed and results shown no differences between males and females when it comes to Industry 4.0 applications understanding. Beside this, students from different departments shown somewhat different sense regarding their knowledge of Industry 4.0 techniques and applications, which may be a big indication of the urgent need of curriculum revision and update. © 2023 IEEE.

关键词： Students

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A Survey About the Decision-making of Self-driving Based on Deep Reinforcement Learning 4

A Survey About the Decision-making of Self-driving Based on ...

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4th international conference on Computer, big data and Artificial Intelligence, ICCBD+AI 2023

作者： Zhang, Jin Ma, Nan Yao, Yongqiang Wang, Cheng Wu, Zhixuan Beijing Union University Beijing Key Laboratory of Information Service Engineering Beijing China Beijing University of Technology Faculty of Information Technology Beijing China Beijing Shuncheng High Technology Corporation Beijing China Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350373233

Self-driving mainly includes four parts: perception, planning, decision-making and control. the decision system is used to output safe and reasonable driving behaviors according to vehicle's state and surrounding environment, so as to autonomously control the vehicle to driving, which is the key part and core of self-driving. With the development of machine learning and AI, the self-driving technology gets further improvement. Using the deep reinforcement learning (DRL) algorithms to solve the behavior decision-making problems of self-driving vehicles has become one of the most concerned research hot spot. Traditional rule-based method lacks the ability to adapt the dynamic environments, DRL can overcome this drawback very well, it has unique advantage for processing those problems. In this paper, we summarize recent years research on DRL for decision making in the field of self-driving and provide a classification of driving tasks using these methods. And analyze in detail the advantages and disadvantages of DRL methods proposed by researchers for solving these driving tasks, and finally summarize some of the challenges and difficulties in applying DRL to complete decision-making problems and look forward to future research works and directions. the study contributes to a deeper understanding of the theories and methods related to DRL and decision-making of self-driving, and provides a reference for future research. © 2023 IEEE.

关键词： Decision making

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