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the Performance Analysis of Several machine learning Methods in Predicting Diabetes

The Performance Analysis of Several Machine Learning Methods...

引用

pattern recognition and Artificial Intelligence (PRAI), international conference on

作者： Yuanyuan Wu Zhuomin Xia Huizhou Liu Mengxing Huang Yu Zhang College of Information and Communication Engineering Hainan University Haikou P.R. China College of Computer Science and Technology Hainan University Haikou P.R. China

Diabetes is a chronic disease characterized by hyperglycemia. Early screening of diabetes patients will help reduce the incidence of diabetes. Accurately modeling the early symptoms of diabetes to reduce the incidence of diabetes has become an urgent problem. therefore, it is a valuable research direction to mine and study the information of diabetes patients, predict the occurrence of noninfectious diseases, and assist doctors to make correct diagnoses. the precision of different data mining techniques has been compared in this research, employing machine learning techniques to predict the incidence of diabetes. the University of California, Irvine’s open-source dataset has been used as the main database for experiment. Different machine learning classifiers such as random forest, neural network and support vector machine have been employed in this experiment. this research gives a technology review of machine learning algorithms that can be used for diabetes prediction through classification. the experimental results indicate that random forest performs better than other machine learning methods.

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Research on a Mixed Asset Identification Model for Industrial control system of Tobacco Industry 24

Research on a Mixed Asset Identification Model for Industria...

引用

4th international conference on Big data, Artificial Intelligence and Risk Management, ICBAR 2024

作者： Yan, Cheng Yang, Cheng Li, Xinjian Ma, Shengjun Zou, Xinhao Chen, Xiaohu Guangshui Cigarette Factory China Tobacco Hubei Industrial LLC Hubei Suizhou China China Tobacco Hubei Industrial LLC Hubei Wuhan China

ISBN: (纸本)9798400709753

With the rapid development of the tobacco industrial internet and the rapid iteration and expansion of industrial assets, it has become a new topic to clarify asset information and sort out asset status in the tobacco industrial environment. Based on common asset types used in the tobacco industrial internet such as PLC, HMI, tobacco machinery, HDMI, and intelligent robots, this paper studies a solution for identifying industrial mixed assets in the tobacco industry by using network communication traffic between assets as the basic data unit, and achieves precise identification of industrial assets in the tobacco industry industrial internet. © 2024 Copyright held by the owner/author(s).

关键词： asset fingerprint recognition model machine learning tobacco asset classification Tobacco asset discovery tobacco asset fingerprint recognition tobacco asset status

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Improving Biomedical Claim Detection using Prompt learning Approaches

Improving Biomedical Claim Detection using Prompt Learning A...

引用

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

作者： Tong Chen Angelos Stefanidis Zhengyong Jiang Jionglong Su School of AI and Advanced Computing XJTLU Entrepreneur College (Taicang) Xi’an Jiaotong-Liverpool University Suzhou Jiangsu China

Biomedical claim detection is an effective method to uncover negative effects arising from the treatment of disease and detect misinformation about medications from online platforms. Due to the power of pre-trained language models (PLMs), such as BERT, RoBERTa and T5, fine-tuned PLMs perform exceptionally well in biomedical claim detection. However, a gap exists in the text classification task between objective forms used in pre-training and fine-tuning for PLMs methods, preventing these models from taking full advantage of the information for biomedical claim detection. Motivated by the prompt learning approach, we propose a method, in which the classification task is transformed into a masked language modeling task that fully utilizes the mask learning capability of PLMs for better prediction of biomedical claim detection. In our method, a template with a mask representing the label is first constructed, and the mask is then filled and mapped to the corresponding label. We use three PLMs as backbone models, i.e., BERT, RoBERTa, and T5, with both hard and mixed templates which are fully and partially predefined templates. Experimental results using the BioClaim dataset demonstrate the superiority of the prompt learning methods over the BERT and RoBERTa classification baselines. Furthermore, the T5 model with mixed template consistently outperforms the rest of experimented models and achieves state-of-the-art performance with an increase of 5.3% on F1-score compared to previous research on this dataset.

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Vision-Based Human Activity recognition Utilizing DCNN and Stacked Multi-stage Multimodal Fusion Strategy

Vision-Based Human Activity Recognition Utilizing DCNN and S...

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international conference on Mechatronics and machine Vision in Practice (M2VIP)

作者： Wenqin Zhu Wei Meng Nian Peng Kun Chen Quan Liu School of Information Engineering Wuhan University of Technology Wuhan China

ISBN: (数字)9798350391916

ISBN: (纸本)9798350391923

Human Activity recognition (HAR) possesses significant potential in pattern recognition, machine learning, wearable computing, computer vision, and notably in rehabilitation assistance. With the ability to automatically track patients' recovery progress, it addresses the issue of insufficient human resources. Presently, improving recognition accuracy hinges on the fusion of multiple information sources like physiological signals, motion sensors, and visual sensors. In this study, a multi-stage multimodal fusion strategy based on stacked integration with Deep Convolutional Neural Network (DCNN) is proposed. Firstly, the inertial sensor and depth sensor data are converted into image data, and the data enhancement method is used to extract edge information using Prewitt, and features are extracted and spliced by the DCNN to realize multimodality at the feature layer. At the decision layer, the two unimodal decisions are fused using the stacking fusion strategy to obtain the final classification decision. the experimental results show that this strategy exhibits significant advantages in human motion recognition, with accuracy rates of 99.85% and 99.51% on Kinect V2 and UTD-MHAD datasets, respectively, exceeding the state-of-the-art deep learning methods on the same dataset.

关键词： Visualization Accuracy Tracking Stacking Sensor fusion Feature extraction data models Human activity recognition data mining Wearable devices

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Research on Hybrid data mining Methods for Social Media Sentiment Analysis 23

Research on Hybrid Data Mining Methods for Social Media Sent...

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Proceedings of the 2023 4th international conference on Big data Economy and Information Management

作者： Haoran Li University of Bristol the United Kingdom

ISBN: (纸本)9798400716669

With the popularity of social media in modern society, online opinion analysis has become an important research field. this study proposes a novel hybrid data mining algorithm that combines sentiment analysis and topic modeling techniques, aiming to improve the accuracy and comprehensiveness of social media data analysis. this paper first introduces the important role of social media in shaping public opinion and the major challenges currently faced in data analysis, such as information overload and viewpoint polarization. this is followed by an overview of the main current research trends in online opinion analysis, especially the application of deep learning and machine learning techniques in processing large-scale social media data. this study details the theoretical foundations and implementation of sentiment analysis and topic modeling, and shows how the two techniques can be combined. through experimental results, this paper demonstrates the performance of the hybrid algorithm in sentiment analysis and topic modeling, and verifies its effectiveness in online public opinion analysis. the experimental results show that the algorithm not only exhibits high accuracy in sentiment categorization, but also demonstrates good performance in topic recognition.

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Detection of Abnormality in Cognitive Test Scores Using Metric learning

Detection of Abnormality in Cognitive Test Scores Using Metr...

引用

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

作者： Song Wang Dania Maryam Waqar Mengyao Hu Richard Gonzalez Yi Lu Murphey University of Michigan-Dearborn Dearborn USA University of Michigan Ann Arbor USA

We investigate deep learning neural networks to detect anomalies in cognitive test scores that are generated by either a trained human coder or a classification program based on the input of clock drawing images. We have developed a system, SN_AD (Siamese Networks for Abnormality Detection) in ordinal classification. SN_AD consists of three Siamese networks, one models class k, and the other two model the two neighboring classes, class k-1 and k+1, and an anomaly detection algorithm, OCAD (Ordinal Class Anomaly Detection). the Siamese networks were trained using metric learning and used to generate class distance distributions. the OCAD algorithm calculates the distances of each test image x to the distributions of class k, k-1, and k+1 using the Siamese networks and detects class score anomalies based on statistical analysis of x being out-of or indistributions of class k and its two neighboring class k-1 and k+1. Experiments were conducted on two sets of images collected through Clock Drawing Test (CDT) in the National Health and Aging Trends Study (NHATS), which is commonly used to detect cognitive impairment. the experimental results show that the proposed system, SN_AD is effective in metric learning of class-specific distributions, detecting coding inconsistencies, and correcting coding errors.

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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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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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