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共 3332 条记录，以下是231-240 订阅

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Research on the Analysis and Prediction Model of Health Monitoring data Based on the Internet of things 4

Research on the Analysis and Prediction Model of Health Moni...

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4th international Signal Processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Zhao, Liling Gong, Zhaomiao School of Nursing Chongqing Medical and Pharmaceutical College Chongqing China Personnel Division Chongqing Medical and Pharmaceutical College Chongqing China

ISBN: (纸本)9798331528676

this research centers on the development of an advanced model for analyzing and forecasting elderly health monitoring data, leveraging the Internet of things (IoT) technology. A comprehensive health monitoring system specifically designed for the elderly is established, enabling the real-time acquisition and transmission of daily health metrics through the integration of IoT technologies. During the data analysis phase, an association rule mining algorithm is utilized to identify hidden patterns and correlations within the elderly health data, thereby facilitating the prediction of health outcomes. A subsequent health prediction model is formulated, incorporating data mining and machine learning methodologies, with its effectiveness and reliability rigorously assessed through simulation experiments. the outcomes of these simulations demonstrate a significant enhancement in both the precision and efficiency of the prediction model. this progress provides a solid scientific basis for the health management of the elderly population. Further model simulations confirmed the accuracy and practicality of the developed prediction model, underscoring its extensive potential for application in elderly health management. ©2024 IEEE.

关键词： Electronic health record

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4th international conference on Mobile Radio Communications and 5G Networks, MRCN 2023

4th International Conference on Mobile Radio Communications ...

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4th international conference on Mobile Radio Communications and 5G Networks, MRCN 2023

ISBN: (纸本)9789819706990

the proceedings contain 60 papers. the special focus in this conference is on Mobile Radio Communications and 5G Networks. the topics include: Naive Bayes Classifier-Based Smishing Detection Framework to Reduce Cyber Attack;a Novel System for Finding Shortest Path in a Network Routing Using Hybrid Evolutionary Algorithm;emotion Analysis and Gender Identification Using Partial Face Detection;data Security threats Arising Between a Cloud and Its Users;machine learning Assisted Software Transplantation: A Baseline Technique;gold and Silver Price Prediction in Indian Market Using machine learning Algorithm;fog-centric IoT Smart Healthcare: Architecture, Applications, and Case Study;responsive Mechanism for Cloud Offloading data Intrusion Detection Using Spark—machine learning Model;Impact of COVID-19 on People;video Analysis Using Deep learning in Smart Gadget for Women Saftey;compression of Medical Images Using Lifting Haar Wavelet Transform for Teleradiology Applications;leveraging Content Based Image Retrieval Using data mining for Efficient Image Exploration;image Enhancement and Restoration: Deep learning for Image Dehazing;hate Speech Detection in Social Media Using Ensemble Method in Classifiers;detection and Classification of Neuro-Degenerative Disease via EfficientNetB7;an Empirical Study of Rainfall Prediction Using Various Regression Models;a Novel Ensemble Approach for Colon Cancer Detection Over the Multiclass Colon dataset;area and Energy Efficient Booth Radix-4 Signed Multiplier Using Verilog;Enhancing Power Quality Improvement Using Model Predictive Controlled System with DPFC;leveraging machine learning for Comprehensive Analysis of Maternal Health: Predicting Health Risks and Improving Antenatal Care;enhancing Information Security for Text-Based data Hiding Using Midpoint Folding Approach: A Comparative Analysis.

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Swarm Intelligence Based Feature Selection and machine learning Methods for Future Railway Traffic Prediction 4

Swarm Intelligence Based Feature Selection and Machine Learn...

引用

4th international conference on Intelligent Technologies, CONIT 2024

作者： Radhika, C. Hanirex, D.Kerana Bharath Institute of Higher Education and Research Department of Computer Applications Tamil Nadu Chennai India Bharath Institute of Higher Education and Research Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350349900

An essential prerequisite for proactive and anticipatory real-time regulation of railroad traffic is the accurate forecast of train delays, or deviations from the schedule. To manage the viability of timetable realization, traffic controllers must forecast the trains' arrival times in (or approaching) their region. Rescheduled travel, traffic control, data on passengers, and effective route design all rely upon the ability to predict train locations in both space and time in real time. the study introduces a data mining system designed to forecast train traffic. the system utilizes a dataset that includes the scheduled times of all trains and the relationships between them, along with weather characteristics. In order to improve the system's accuracy in predicting future traffic, research was proposed that would use Swarm Intelligence-based feature selection techniques as Harris Hawk Optimization (HHO). To ascertain the best predictive models in terms of predictive capability, machine learning techniques namely Bi-directional Long Short-Term Memory (Bi-LSTM) with entity embedding are utilized. It has been demonstrated that the suggested algorithm for processing and analyzing rail traffic is more efficient than alternative approaches and has shown an accuracy rate of 92.3% and precision of 93%. © 2024 IEEE.

关键词： Swarm intelligence

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4th international conference on Recent Trends in Advanced Computing - Computer Vision and machine Intelligence Paradigms for Sustainable Development Goals, ICRTAC-CVMIP 2021

4th International Conference on Recent Trends in Advanced Co...

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4th international conference on Recent Trends in Advanced Computing - Computer Vision and machine Intelligence Paradigms for Sustainable Development Goals, ICRTAC-CVMIP 2021

ISBN: (纸本)9789811971686

the proceedings contain 30 papers. the special focus in this conference is on Recent Trends in Advanced Computing - Computer Vision and machine Intelligence Paradigms for Sustainable Development Goals. the topics include: Advanced Algorithmic Techniques for Topic Prediction and Recommendation—An Analysis;Implementation of an Automatic EEG Feature Extraction with Gated Recurrent Neural Network for Emotion recognition;high Performance Classifier for Brain Tumor Detection Using Capsule Neural Network;mining Suitable Symptoms to Identify Disease Using Apriori and NBC;background Features-Based Novel Visual Ego-Motion Estimation;livspecs: Design and Implementation of Smart Specs for Hearing and Visually Challenged Persons;Self-balancing Robot Using Arduino and PID Controller;a Survey Based on Online Voting System Using Blockchain Technology;convergence Perceptual Model for Computing Time Series data on Fog Environment;survey on Collaborative Filtering Technique for Recommender System Using Deep learning;a Survey on Power Consumption Indicator Using machine learning-Based Approach;a Novel Hand Gesture recognition for Aphonic People Using Convolutional Neural Network;comprehensive Analysis of Defect Detection through Image Processing and machine learning for Photovoltaic Panels;covid Analysis Prediction Using Densenet Method in Deep learning;Feature Extraction Based on GLCM and GLRM Methods on COVID-19 dataset;memory Augmented Distributed Monte Carlo Tree Search Algorithm-Based Content Popularity Aware Content Recommendation Using Content Centric Networks;Enhancing Protection Against Scalper Bots with ML;An Enhanced Optimized Abstractive Text Summarization Traditional Approach Employing Multi-layered Attentional Stacked LSTM with the Attention RNN;implementation of machine learning Methods on data to Analyze Emotional Health;SMS Spam Classification Using PSO-C4.5.

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Research on Academic Risk Early Warning Model Based on the Analysis of learning Behavior data 4

Research on Academic Risk Early Warning Model Based on the A...

引用

4th international conference on Computer Science and Blockchain, CCSB 2024

作者： Cao, Chang Yang, Qiping Liu, Qingqing Liang, Jing Xie, Shuxiao Network & Educational Technology Institute Jinan University Guangzhou China

ISBN: (纸本)9798350356502

Accelerated by the rapid advancement of educational digital transformation, learners generate a wealth of process data during online learning. this study aims to employ educational data mining and machine learning methodologies to achieve early warnings for student academic risks. Utilizing the platform of Guangdong-Hong Kong-Macao Greater Bay Area University Online Open Course Alliance, the research collects and analyzes learning behavior data, extracting behavioral indicators closely related to student academic performance. through an optimized BP neural network algorithm, a grade prediction model is constructed that uses student academic performance ranks as output features and determines input features via Spearman correlation analysis;hyper-parameters of the hidden layer are optimized using Python and MATLAB, followed by model evaluation and prediction, ultimately resulting in a student academic risk warning model. this study defines and divides student behavior data based on Flanders Interaction Analysis theory, extracting characteristic data with representational significance. Compared with other models, this model is more pertinent in summarizing learning behavior and predicting academic performance. Experimental results demonstrate that the model exhibits excellent performance in accuracy (93.63%), recall rate (93.71%), and F1-score (0.935). this model effectively identifies students at academic risk and provides targeted early warnings, thereby reducing their academic risks. © 2024 IEEE.

关键词： Contrastive learning

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4th international conference on Digital Technologies and Applications, ICDTA 2024

4th International Conference on Digital Technologies and App...

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4th international conference on Digital Technologies and Applications, ICDTA 2024

ISBN: (纸本)9783031686498

the proceedings contain 53 papers. the special focus in this conference is on Digital Technologies and Applications. the topics include: Enhancing Student Success in Physical Education through Educational data mining Techniques;a Fuzzy Timed Petri Net Approach to Modeling Forward Collision Warning Systems Based on the 3-Second Rule;a Systematic Literature Review on Regression machine learning for Urban Flood Hazard Mapping;leveraging Artificial Intelligence for Environmental Information Integration in Investment Decision Making;leveraging Deep Collaborative Filtering for Advanced Recommender Systems;Is AI an Effective "learning Tool" in Academic Writing? Investigating the Perceptions of third-Year University Students on the Use of Artificial Intelligence in Classroom Instruction;Understanding the Attitude of Teacher Education Students Toward Utilizing ChatGPT as a learning Tool: A Quantitative Analysis;machine learning and Big data for Cybersecurity: Systematic Literature Review;Leveraging machine learning for Signal Processing in Surface Electromyography (sEMG) for Prosthetic Control;RPL-Shield: A Deep learning GNN-Based Approach for Protecting IoT Networks from RPL Routing Table Falsification Attacks;UWB Slotted Rectangular Antenna Gain Enhancement Using AMC Metasurface Layer for Wireless Communication Devices;understanding the Digital Frontier: Examining Privacy and data Security in Desktop Web Browsers;optimizing Medium Access Protocols: A Comprehensive Node-Level Performance Analysis Exploring Network Density and data Length;Ensuring Hybrid Pedagogical Continuity in Moroccan Secondary Education Schools amidst the Pandemic: Impacts on EFL Students’ Academic Performances;entrepreneurial, Organizational, and Sectoral Dynamics of Industrial Innovation: Case of Moroccan Firms;digital Transformation: What's the Reality in the Moroccan University Context?;the University and Territorial Entrepreneurial Ecosystem.

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Advances in Big data and data mining: Techniques and Applications in data Fusion for Enhanced Insights and Decision-Making 4

Advances in Big Data and Data Mining: Techniques and Applica...

引用

4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Maindola, Meenakshi Al-Fatlawy, Ramy Riad Kumar, Rakesh Boob, Nandini Shirish Sreeja, S.P. Sirisha, N. Graphic Era Deemed to be University Department of Computer Science & Eng. Dehradun India College of Technical Engineering The Islamic University Department of Computers Techniques Eng. Najaf Iraq Gla University Department of Computer Eng. & Applications Mathura India Saveetha Institute of Medical and Technical Sciences Saveetha School of Eng. Tamilnadu Chennai India New Horizon College of Engineering Bangalore India Mlr Institute of Technology Department of Computer Science and Eng. Telangana Hyderabad India

ISBN: (纸本)9798331518981

Big data's explosive growth has transformed several businesses by making it possible to glean insightful information from big, complex databases. this study examines new developments in big data and data mining methods, concentrating on data fusion as a means of improving decision-making. data fusion, the act of combining many data sources to get more accurate and thorough insights, is becoming essential in industries including cybersecurity, healthcare, finance, and marketing. the study looks at how cutting-edge data mining methods, such as anomaly detection, classification, and clustering, might increase the efficiency of data fusion. Important issues are also covered, including managing heterogeneous data, guaranteeing data quality, and scalability. this paper shows how data fusion approaches may enhance decision-making processes by offering more comprehensive perspectives and useful insights via an analysis of real-world applications. the paper's conclusion discusses potential future developments, emphasising how machine learning and artificial intelligence may be used to improve data mining and fusion procedures, potentially leading to even higher levels of precision and effectiveness in decision-making across a range of industries. © 2024 IEEE.

关键词： data accuracy

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A Learner Behavior recognition and Detection Method based on Human Posture Estimation 4

A Learner Behavior Recognition and Detection Method based on...

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4th international conference on Computer Graphics, Image, and Virtualization, ICCGIV 2024

作者： Fu, Chen School of Smart Health Chongqing Polytechnic University of Electronic Technology Chongqing401331 China

ISBN: (纸本)9781510683242

With the rapid development of information technology and smart classroom, the accuracy of learner behavior recognition in classroom has become more and more important. In the process of behavior recognition by means of information technology, including deep learning technology, it is often difficult to accurately identify learners' actions due to limb occlusion, motion blur and camera position. Meanwhile, frequent classroom management with the help of teachers will also lead to class interruption and destroy the overall fluency of teaching. Based on this problem, this work uses an improved pose estimation method based on single-stage key points and pose detection algorithm to classify and judge the movements of learners in the classroom for problems such as limb occlusion and motion ambiguity, so as to achieve the purpose of assisting the management of classroom teaching process. Finally, the results of the behavior recognition detection method proposed in this paper are compared with the marked action image data, and the experimental data show that the proposed method can meet the needs of learners' behavior recognition detection in the classroom to a certain extent. © 2024 SPIE.

关键词： Deep learning

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Identification of Alzheimer's Disease Associated Genes through Explicable Deep learning and Bioinformatic 4

Identification of Alzheimer's Disease Associated Genes throu...

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4th IEEE international conference on pattern recognition and machine learning, PRML 2023

作者： Sun, Xiangrong Shu, Wei Zhang, Yuxin Huang, Xiansheng Liu, Juxia Liu, Yuan Yang, Tiejun Guilin Medical University Department of Intelligent Medicine College of Intelligent Medicine and Biotechnology Key Laboratory of Medical Biotechnology and Translational Medicine of Guangxi Education Department Guilin China Guilin Medical University College of Intelligent Medicine and Biotechnology Department of Intelligent Medicine Guilin China

ISBN: (纸本)9798350324303

We utilized interpretable deep learning to identify key genes and potential biomarkers associated with Alzheimer's disease (AD). AD-related gene expression data from GEO datasets and Alzdata were collected. To create a training set, we performed differential expression analysis to delete low-expressed genes. To explore disease-related genes and potential biomarkers, we employed a pathway-centric deep learning network, which integrated gene expression data with established biological pathways, building upon the foundation of PASNet. Our study employed a pathway-related deep learning model based on PASNet combined with bioinformatic analysis for AD risk prediction, and achieved a good performance (AUC = 0.82, F1 score = 0.73). the model accurately classified AD patients based on gene expression data from the brain tissue while providing interpretability deep neural networks. Potential biomarkers (DYNC1I1, DNAJC1, SCRIB, TFEB, MAPKAPK3) and biological pathways (Senescence-Associated Secretory Phenotype, Transcriptional activity of SMAD2/SMAD3:SMAD4 heterotrimer, Toll-like Receptor Cascades) associated with AD were identified. We have employed a combination of bioinformatics analysis and deep learning models to identify a series of candidate genes associated with the diagnosis of Alzheimer's disease (AD). Moreover, we have validated their potential as biomarkers through the utilization of external datasets, and finally focus on the five genes DYNC1I1, DNAJC1, SCRIB, TFEB, and MAPKAPK3. © 2023 IEEE.

关键词： Risk assessment

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4th international conference on Digital Technologies and Applications, ICDTA 2024

4th International Conference on Digital Technologies and App...

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4th international conference on Digital Technologies and Applications, ICDTA 2024

ISBN: (纸本)9783031686597

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