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Research on Tibetan Archaeological Knowledge Search System Based on Knowledge Graph

Research on Tibetan Archaeological Knowledge Search System B...

引用

pattern recognition and machine learning (PRML), IEEE international conference on

作者： Nuo Qun Tianhao Shan Gadeng Lobsang Xiaohong Yu Tashi Nyima Collaborative Innovation Center Tibet informatization by MOE and Tibet Autonomous Region School of Information Science and Technology Tibet University Lhasa China School of Information Science and Technology Tibet University Lhasa China Xizang Minzu University Xianyang Shaanxi China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

As an important ethnic area with a long history and rich cultural heritage, the inheritance and protection of archaeological culture in Tibet has always been highly concerned at home and abroad. this study provides a systematic and comprehensive knowledge system for Tibetan archaeology by constructing knowledge graph of Tibetan cultural relics, combining named entity recognition algorithm and developing knowledge retrieval system. A TB-CRM data model suitable for Tibetan characteristics was created, and Python and Neo4j were used for data processing and storage. through the comparison and optimization of named entity recognition models, high precision information retrieval is realized. In the end, a full-featured and user-friendly retrieval system was developed to serve the research needs of both heritage lovers and professional scholars. the results of this study provide important support for the protection and inheritance of Tibetan cultural heritage and the digitization of archaeology, and look forward to the broad prospects brought by technological progress for future archaeological research.

关键词： Archeology Systematics Knowledge based systems Named entity recognition Knowledge graphs machine learning pattern recognition Cultural differences Protection Optimization

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Classroom learning Situation Analysis System Based on Computer Vision 4th

Classroom Learning Situation Analysis System Based on Comput...

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4th international conference on machine learning, Image Processing, Network Security and data Sciences (MIND)

作者： Chen, Yixin Hu, Xi Jianghan Univ Sch Artificial Intelligence Wuhan Hubei Peoples R China

ISBN: (纸本)9783031243660;9783031243677

With the continuous progress of society and the change of learning methods and teaching environment, research on the analysis of College Students' learning situation in the classroom is emerging in endlessly. the real-time learning situation analysis (LSA) system of college students mainly analyzes college students' learning status and behavior in the classroom, which can provide college teachers with the basis and means to effectively manage the classroom. Based on computer vision (CV) technology, this paper takes the students' listening behavior in college theory class as the research object, and uses CAMSHIFT tracking algorithm to analyze the behavior of college students in class. On this basis, the system of classroom learning situation analysis (CLSA) based on CV is designed and implemented. through the simulation experiment, the traditional algorithm is compared with the CV CAMSHIFT tracking algorithm;the test results show that the recognition rate of this algorithm is about 10% higher in the case of facial interference, which proves the accuracy, availability and effectiveness of the classroom real-time LSA system designed and studied in this paper, and provides a new way of LSA for college teachers and students.

关键词： Computer vision Classroom CAMSHIFT tracking algorithm learning situation analysis

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4th international conference on Smart Applications and data Analysis, SADASC 2022

4th International Conference on Smart Applications and Data ...

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4th international conference on Smart Applications and data Analysis, SADASC 2022

ISBN: (纸本)9783031204890

the proceedings contain 35 papers. the special focus in this conference is on Smart Applications and data Analysis. the topics include: Investigation of Different Speed Controllers to Improve the Performance of Vector-Controlled Synchronous Reluctance Motor;performing Energy-Efficient Motions for Wheeled Mobile Robots by Designing an Orientation Controller;study of Path Optimization of an Electric Vehicle: Case of Morocco;scalable Meta-Bayesian Based Hyperparameters Optimization for machine learning;a Novel Graded Multi-label Approach to Music Emotion recognition;data-Driven Solutions for Electricity Price Forecasting: the Case of EU Improvement Project;determination of the Probability of Factors Occurrence Impacting Warehouse Planning by Bayesian Networks;smart Grid Production Cost Optimization by Bellman Algorithm;Sliding Mode Control of Six-Switch Five-Level Active Neutral Point Clamped (6S-5L-ANPC);Automatic SPECT Image Processing for Parkinson’s Disease Early Detection;Statistical Analysis of PV-Wind-Battery Hybrid System Energy Efficiency for Green Buildings Power Supply;Energy Management in a Connected DC Microgrid Using Fuzzy Controller;approach for Optimisation Warehouse Storage Areas Based on the Container Storage Problem;Roadmap to Implement Industry 5.0 and the Impact of this Approach on TQM;artificial Intelligence Based Plastic Injection Process for Initial Parameters Setting and Process Monitoring-Review;the Benefits of Combining Digitalization with Quality Tools: Application in the Field of Wiring Systems Manufacturing for the Automotive Industry;digital Transformation of the Flotation Monitoring Towards an Online Analyzer;air Quality Remote Monitoring Module: I4.0 Application in Smart Poultry Farm;smart Greasing System in mining Facilities: Proactive and Predictive Maintenance Case Study;Risk Management Based on Hybridized TOPSIS Method Using Genetic Algorithm;graph Convolution Networks for Unsupervised learning;blockchain Application Methodology for

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Comprehensive Performance Evaluation of Association Rule mining Algorithms for Border Gateway Protocol Anomaly Detection

Comprehensive Performance Evaluation of Association Rule Min...

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Emerging Smart Technologies and Applications (eSmarTA), international conference on

作者： Mubarak Altamimi Nehad Ramaha Akram A. Almohammedi Almaafer Communıty College Taiz Yemen Karabük University Karabük Turkey Dept. of Computer Engineering Karabuk University Karabük Turkey Dept. of Electrical & Electronics Engineering Karabük University Karabük Turkey

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Association rule mining is a data mining technique that concentrates on uncovering noteworthy connections or associations within a vast database of items. the process entails identifying frequently occurring sets of items and deriving association rules from those sets. Border Gateway Protocol (BGP) serves as the prevalent gateway protocol facilitating communication between autonomous systems, enabling the exchange of routing and reachability information. the occurrence of anomalous behavior in protocol attributes can be attributed to diverse factors such as insufficient provisioning, malicious attacks, traffic or equipment problems, and errors made by network operators. Based on the association rule, the dataset is examined and partitioned into sub-frequent pattern datasets. Each pattern dataset selects a single anomalous frequent itemset for comparison to determine the itemset with the highest anomaly value. the commonly used rule-based machine learning algorithms for anomaly detection include Frequent pattern (FP) growth and Apriori algorithms. this study aims to use unsupervised algorithms based on association rules in select features to detect BGP anomalies. Additionally, the performance of these algorithms will be evaluated in terms of support, confidence, and accuracy values. Furthermore, the anomaly detection results were presented using a tailored and efficient tool and framework, enhancing the clarity and providing a more precise and detailed understanding of the performance.

关键词： Performance evaluation machine learning algorithms Itemsets Logic gates Routing Feature extraction Border Gateway Protocol

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machine learning and Artificial Intelligence - Proceedings of MLIS 2022

Machine Learning and Artificial Intelligence - Proceedings o...

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4th international conference on machine learning and Intelligent Systems, MLIS 2022

ISBN: (纸本)9781643683560

the proceedings contain 20 papers. the topics discussed include: attitudes towards online learning during COVID-19: a cluster and sentiment analyses;factors influencing the animation infographic and augmented reality technique in healthcare promotion communication for the elderly in thailand;empirical evaluation of the asymptotic behavior of the analysis complexity of hard random 3-CNF formulas;machine learning-based current density simulation for direct borohydride fuel cell;applying social network analysis and data mining techniques to support decision-making: a case study;data-driven fault classification of induction motor based on recurrence plot and deep convolution neural network;wavelet pooling scheme in the convolution neural network (CNN) for breast cancer detection;generalized multiquadric neural networks in image reconstruction;and numerical investigation on three local-adaptive k-point multiquadric neural networks.

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Sign Language recognition Using Deep learning 4

Sign Language Recognition Using Deep Learning

引用

4th international conference on Cognitive Computing and Information Processing, CCIP 2022

作者： Nirmala, M. New Horizon College of Engineering Computer Science and Engineering Bangalore India

ISBN: (数字)9781665456487

ISBN: (纸本)9781665456487

the ability to converse with hearing and deaf persons has always been difficult for those who are tongue-Tied. In this paper, we can see different methods which are introduced to help them to communicate effectively. there are many human interpreters or assistant tools to help them communicate, but each person cannot afford that aid. the only mode of communication for them is sign language. therefore, the project's primary goal is to assist those individuals by providing a system that will recognize the signs, translate them into text, and enable them to lead a normal social life. Previously, a method including hand detection had been developed as a learning tool for novices in sign language. the system was developed using a method based on skin color modeling known as explicit skin-color space thresholding. the specified range of skin tones will distinguish between pixels, or the hand, and non-pixels, or the background. the photos were given as input to a model called the CNN a deep learning algorithm. We will be implementing this project using Keras to train the images. this document provides information on a variety of projects/research on sign language detection in the domains of machine learning, deep learning, and image depth data. this study considers a number of the numerous problems that must be overcome in order to overcome this problem, as well as the future scope. © 2022 IEEE.

关键词： Pixels

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A Novel Procedure for Recognising Human Activity Using machine learning

A Novel Procedure for Recognising Human Activity Using Machi...

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Smart Electronics and Communication (ICOSEC), international conference on

作者： Neelam Singh Mansi Bhatt Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Application Graphic Era Deemed to be University Dehradun India

In the past few years it is seen that there has been a boom in the usage of smart devices consisting of sophisticated sensors such as gyroscope or gyro and accelerometer. Human Activity recognition (HAR) is one of these sensors' applications. Given the dependence on general machine learning (ML) techniques, Deep learning techniques are suggested by current research to be suitable for feature extraction from unprocessed sensor data. the study focuses on Human Activity recognition (HAR) by means of data gathered from smartphone accelerometer sensors and employs Long-Short Term Memory (LSTM) networks in the time-series domain. Furthermore, a hybrid network consisting of a 2-layer RNN-LSTM is proposed to enhance the accuracy of recognition performance. For the input and output activation function in the neural network ReLU and Softmax function respectively are used.

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Adaptability of Simple Classifier and Active learning in Music Emotion recognition 2021

Adaptability of Simple Classifier and Active Learning in Mus...

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4th international conference on Electronics, Communications and Control Engineering (ICECC)

作者： Yu, Xiaoxiao Beijing Univ Technol Beijing Dublin Int Coll Beijing 100000 Peoples R China

ISBN: (纸本)9781450389129

this article selects five common classifiers in music emotion recognition, namely SVM (kernel function: RBF), SVM (kernel function: LINEAR), kNN (K = 3) and kNN (K = 5), as well as GMM, application active learning performs music emotional classification. the experiment uses 365 music clips in the PMEmo data set, and after manual labeling, they are divided into four groups: HAPPY, SAD, ANGRY and RELAX according to the thayer emotion model and the classification effect is judged by comparing F1-measure. Experiments show that active learning can reduce the need for manual labeling by about 78%, and it can help to achieve better results with fewer labels. At the same time, among the three simple classifiers, the SVM model and active learning have a better degree of adaptation in the field of music emotion recognition.

关键词： Music emotion recognition machine learning Active learning

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Artificial Intelligence and machine learning in Human Resource Management and Market Research for Enhanced Effectiveness and Organizational Benefits 4

Artificial Intelligence and Machine Learning in Human Resour...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Indoria, Devadutta Narendra Kiran, P.B. Kumar, Anuj Goel, Manjusha Shelke, Nitin Arvind Singh, Jagendra Vikram Dev University Department of Commerce Jeypore India Mittal School of Business Lovely Professional University Phagwara India Ajay Kumar Garg Engineering College Department of Computer Science and Engineering Ghaziabad India Raj Kumar Goel Institute of Technology Department of Management Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350306118

this study investigates the substantial implications of cutting-edge technology on forecasting market demand, improving safety, and human resource management (HRM). the study simplifies the candidate selection method by applying Long Short-Term Memory (LSTM) models to automate application screening. the use of Arduino micro controllers in conjunction with temperature, fire, and smoke sensors increases real-time reactions to potential dangers for improving safety in resource management. Artificial neural networks (ANN) are used to forecast market demand based on data from automotive sales from 2016 to 2023. the outcomes demonstrate how effective each technique was. LSTM-driven applicant screening demonstrates the capacity to boost HRM by accelerating the recruiting process. the integration of Arduino controllers demonstrates rapid and precise risk recognition, enhancing industrial safety. When it comes to forecasting automotive sales for 2022, the ANN model, in particular, predicts market demand with astonishing precision, achieving a 100% accuracy rate. this implies that it is capable of strategic planning and resource management. these findings underscore the critical role that AI-driven systems play in improving organisational performance, security, and decision-making, as well as the revolutionary potential of AI -driven systems across a wide range of industries. By acting as a sobering reminder of the dynamic interplay that happens between industry and technology, the research lays the path for a more safe and successful future. © 2023 IEEE.

关键词： Human resource management

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Traffic improvisation by identifying the accident locations using ML/data mining approaches 4

Traffic improvisation by identifying the accident locations ...

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4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Goswami, Nitish Giri Sharma, Pranav Arora, Aditi Singh, Nidhi Abes Engineering College Department of Computer Science U.P. Ghaziabad India Abes Engineering College U.P. Ghaziabad India Krishna Engineering College U.P. Ghaziabad India

ISBN: (纸本)9781665474368

Road Traffic Accident Prediction is really widely necessary element that affects the premature death of lots of people and also have the losses of property which is either public or private. Traffic road safety basically is a word which is related to the proper planning and the executing the various approaches to reduce the cause of road traffic accidents. Road traffic accident survey is really most essential to pick out diverse elements connected with road accidents that can surely help in decreasing the accident rate. the homogeneity of road traffic accident data is major problem in traffic road safety survey. Primarily in this paper we are using decision tree, random forest classifier on a current traffic road accident data. Although, in these defined procedures they are efficiently fitted to pull out homogeneity of road traffic accident data. Several studies center on procedure of road accidents. Other elements put in weather and light conditions of the road. Road accident harshness keeps on changing over a period of time and also expand infinitely. Hence in this study we basically examined latest works, data mining methodologies and tools that were demonstrated better in accident factual data analysis and prediction. We also made a web app using flask to deploy our machine learning model and use this app to predict and send a message to alert the user. © 2022 IEEE.

关键词： data mining

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