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检索条件"任意字段=Proceedings of the 6th International Conference on Predictive Models in Software Engineering"

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Integrated Healthcare System for Saline Monitoring, Patient Communication, and Heart Disease Prediction Using IoT and Machine Learning Algorithms 6

Integrated Healthcare System for Saline Monitoring, Patient ...

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6th IEEE international conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Amune, Amruta Chavanke, Shraddha Joshi, Shushwet Kuttarmare, Shubham Kathane, Shubham Vishwakarma Institute of Technology Department of Electronics and Telecommunication Engineering Pune India

ISBN: (纸本)9798350306613

the integration of IoT and ML technologies in the healthcare sector has brought significant transformations, enhanced patient care and reducing costs. this research paper presents an innovative idea focuses on developing a comprehensive system for healthcare, incorporating saline fluid control and monitoring, patient communication, and predictive analysis for heart disease. the saline control and monitoring feature employs an ultrasonic sensor to detect fluid levels, with data processed by a NodeMCU. When fluid levels exceed a threshold, alerts are triggered through a buzzer, red LED, and WhatsApp messages sent to medical authorities and a designated relative. the integration of WhatsApp's API bot streamlines messaging. Additionally, to ensure patient safety, a servo motor clamps the saline tube if medical authorities don't respond promptly, preventing blood backflow. the system also includes a glove with sensors for communication among bedridden patients. Heartbeat and temperature sensors monitor vital signs and medical history, transmitted to a software interface developed using ML algorithms and a Java-based GUI. this facilitates data management and analysis for medical authorities, enabling the prediction of heart disease likelihood based on new patient data using ML algorithms. the research paper aims to enhance healthcare efficiency, improve patient care, and reduce response time during critical situations. the key findings demonstrate successful implementation of a comprehensive healthcare system monitoring saline fluid, providing timely alerts, enabling patient communication, and predicting heart disease likelihood. In conclusion, the system utilizes advanced technologies to enhance patient safety, facilitate communication, and assist medical authorities in making informed decisions. © 2024 IEEE.

关键词： Artificial intelligence Disease prediction Healthcare system Healthcare technology IoT Machine learning Patient communication Patient safety Saline controlling and monitoring Sensor-embedded glove WhatsApp's API bot

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Insights on Modelling Physiological, Appraisal, and Affective Indicators of Stress using Audio Features 44

Insights on Modelling Physiological, Appraisal, and Affectiv...

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44th Annual international conference of the IEEE-engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Triantafyllopoulos, Andreas Zaenkert, Sandra Baird, Alice Konzok, Julian Kudielka, Brigitte M. Schuller, Bjoern W. Univ Augsburg EIHW Chair Embedded Intelligence Hlth Care & Well Augsburg Germany Univ Regensburg Inst Psychol Regensburg Germany Hume AI New York NY USA Imperial Coll GLAM Grp Language Audio & Mus London England

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Stress is a major threat to well-being that manifests in a variety of physiological and mental symptoms. Utilising speech samples collected while the subject is undergoing an induced stress episode has recently shown promising results for the automatic characterisation of individual stress responses. In this work, we introduce new findings that shed light onto whether speech signals are suited to model physiological biomarkers, as obtained via cortisol measurements, or self-assessed appraisal and affect measurements. Our results show that different indicators impact acoustic features in a diverse way, but that their complimentary information can nevertheless be effectively harnessed by a multi-tasking architecture to improve prediction performance for all of them.

关键词： Biological system modeling Human factors predictive models Biomarkers Acoustic measurements Multitasking Physiology

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Stepwise Regression Machine Learning models for In-Hospital Mortality Prediction in Patients After ST-Segment Slevation Myocardial Infarction (STEMI) 24

Stepwise Regression Machine Learning Models for In-Hospital ...

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24th IEEE/ACIS international Winter conference on software engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2022

作者： Cheng, Chi-Yung Chiu, I-Min Lin, Chun-Hung Richard Lin, Xin-Hong Chen, Fu-Cheng Hsu, Ting-Yu National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung Taiwan Kaohsiung Chang Gung Memorial Hospital Department of Emergency Medicine Kaohsiung Taiwan

ISBN: (纸本)9798350310412

Acute myocardial infarction is a leading cause of cardiogenic shock and mortality. the aim of current study is to identify factors and develop machine learning models that predicts in-hospital mortality of ST-segment elevation myocardial infarction (STEMI) patients in South-East Asian population. this is a single center, retrospective study, from patients presenedt to the emergency room at Kaohsiung Chang Gung Memorial Hospital, Taiwan. the study included non-trauma adults (≥20 years of age) who were diagnosed with acute STEMI. A total of 1567 patients who met the inclusion criteria were enrolled. the area under the receiver operating characteristic curve was 0.839 in logistic regression (LR) and 0.825 in random forest (RF). the accuracy was 0.821 in LR and 0.812 in RF. the sensitivity and specificity were 0.883 and 0.815 in LR, and 0.875 and 0.806 in RF. In conclusion, the predictive models developed using LR and RF algorithms can be used to predict the risk of in-hospital death for STEMI patients. © 2022 IEEE.

关键词： Hospitals

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Dual Graph Convolutional Networks for Document-Level Event Causality Identification 1

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6th Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint international conference on Web and Big Data (APWeb-WAIM)

作者： Liu, Yang Jiang, Xiaoxia Zhao, Wenzheng Ge, Weiyi Hu, Wei Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China Sci & Technol Informat Syst Engn Lab Nanjing Jiangsu Peoples R China

ISBN: (数字)9783031251986

ISBN: (纸本)9783031251979;9783031251986

Event causalities organize events into a graph according to causal logics, which assists humans in decision making by causal reasoning among events. Despite many efforts to identify event causalities, most of them assume that only one causality exists in a sentence or causalities only occur in adjacent sentences, leading to the incapability of detecting multiple causalities or document-level causalities. In this paper, we propose a novel model for document-level event causality identification named DocECI. We define two heterogeneous document graphs, namely text structure graph and mention relation graph, and encode them with relational graph convolutional networks, which gradually aggregate the information of multi-granular nodes in a cascade manner and capture the causality patterns. Experiments on a benchmark dataset show that DocECI outperforms existing models by a significant margin. Moreover, a new experiment is conducted on causality direction identification, which is overlooked by existing models.

关键词： Event causality Document graph R-GCN

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Integrated Load Modeling of Induction Motors Considering Frequency Characteristics 23

Integrated Load Modeling of Induction Motors Considering Fre...

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proceedings of the 6th international conference on Information Technologies and Electrical engineering

作者： Jinshi Yang Shifang Hai Pengpeng Yu Zuqiang Bai Fan Xiao High-tech Institute Fan Gong-ting South Street on the 12th China

ISBN: (纸本)9798400708299

In response to the current problem of limited frequency characteristic analysis in load modeling, this article establishes three models: static load model, dynamic load model and comprehensive load model considering distribution network branches. Analyzes the mechanism of the three models, uses MATLAB and PSD-BPA software to establish electromagnetic transient and electromechanical transient simulation models of power system load respectively, and simulates and analyzes the influence of internal parameters, dynamic load ratio and equivalent parameters of the distribution network on the load of the dynamic load model. the accuracy of the theoretical derivation is verified by using the simulation data to calculate the frequency factor under different models and comparing the simulation results with the theoretical derivation.

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Analysis of two variants of U-net for pulmonary nodule segmentation: Attention U-net and Dense-Attention U-net 4

Analysis of two variants of U-net for pulmonary nodule segme...

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4th international conference on Signal Processing and Machine Learning, CONF-SPML 2024

作者： Tang, Jingheng Zhang, Yixiao Li, Shaofan Department of Glasgow College University of Electronic Science and Technology of China Sichuan 611731 China Department of Software Engineering Qingdao University Shandong 266071 China

ISBN: (纸本)9781510674721

Lung nodules, solid or subsolid lung masses smaller than 3 centimetres, including subtle nodules within complex lung tissue, can go unnoticed by medical professionals due to fatigue or limited expertise. To address this challenge, our study proposes an algorithm for lung region of interest (ROI) computed tomography (CT) image processing based on the Attention U-net architecture and an enhanced variant called Dense-Attention U-net. the Attention U-net incorporates Attention Gates in the decoding path, facilitating the passage of relevant information while reducing irrelevant learning. We evaluate model performance using Dice loss and receiver operating characteristic (ROC) curve analysis. the Dense-Attention U-net enhances the model with dense connectivity in both encoder and decoder sections, ensuring complete layer connections. We used a dataset of 27,190 lung CT images for evaluation. Both U-net variants perform well, with the Dense-Attention U-net outperforming the Attention U-net. the Attention U-net took about eight hours to reach a training loss of 0.13, while the Dense-Attention U-net achieved the same in just half an hour. Notably, the Dense-Attention U-net achieves higher predictive accuracy, with area under the curve (AUC) values of 0.94 and 0.91 for the ROC curves, respectively. Visual results demonstrate excellent segmentation performance for both models. In conclusion, our study introduces and analyses two U-net variants for pulmonary nodule segmentation, emphasizing attention mechanisms and dense connections to enhance feature focus and model efficiency. We acknowledge challenges such as dataset biases and suggest future research directions, including individual nodule labeling and quantification, to enhance diagnostic accuracy. © 2024 SPIE.

关键词： Computerized tomography

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Groundwater Level Forecasting Using Spatio-Temporal Graph Convolutional Networks (GCNs) and XGBoost

Groundwater Level Forecasting Using Spatio-Temporal Graph Co...

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Mobile Computing and Sustainable Informatics (ICMCSI), international conference on

作者： G. Naga Satish Vijaya Chandra Jadala Kolisetti Giridhara Sravani Sangeetha Allam Shanmuga Sundari M Jyothirmai thada Computer Science and Engineering BVRIT HYDERABAD College of Engineering for Women Hyderabad India Department of Computer Science and Artificial Intelligence School of Computer Science and Artificial Intelligence SR University Warangal Department of AIML Gokaraju Rangaraju Institute of Engineering And Technology Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

this research analyzes groundwater levels across multiple districts using data from over 100 observation wells in each district. To capture seasonal variations and predict groundwater behavior, this research has developed three models: periodic, polynomial, and rainfall-based. the periodic and polynomial models describe groundwater level fluctuations based on historical well data, while the rainfall model assesses the influence of precipitation on water levels in the wells. In addition, this research explores advanced predictive techniques by incorporating Spatio-temporal Graph Convolutional Networks (GCNs) and XGBoost. these methods enable a more nuanced understanding of spatio-temporal dependencies and improve predictive accuracy by leveraging both spatial relationships among wells and the temporal evolution of groundwater levels. the integration of traditional models with machine learning techniques aims to enhance groundwater management and inform decision-making.

关键词： Rain Graph convolutional networks Groundwater Machine learning Forestry predictive models Polynomials Data models Informatics Mobile computing

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Estimation of Minimum Miscibility Pressure in Impure/Pure N2 Based Enhanced Oil Recovery Process: A Comparative Study of Statistical and Machine Learning Algorithms

Estimation of Minimum Miscibility Pressure in Impure/Pure N2...

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Robotics, Control and Automation engineering (RCAE), international conference on

作者： Xiuli Zhu Seshu Kumar Damarla Biao Huang Department of Control University of Shanghai for Science and Technology Shanghai China Department of Chemical and Materials Engineering University of Alberta Alberta Canada

Minimum miscibility pressure (MMP) prediction plays an important role in design and operation of nitrogen based enhanced oil recovery processes. In this work, a comparative study of statistical and machine learning methods used for MMP estimation is carried out. these methods include weighted k-nearest neighbors, wavelet neural network, general regression neural network, extreme learning machine, regression trees (RTs), random forest, bagging RTs, boosting RTs and Gaussian process regression. Most of the predictive models developed in this study exhibited superior performance over predictive models reported in literature.

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Bayesian Model Averaging for Improving the Accuracy of Cuffless Blood Pressure Estimation 44

Bayesian Model Averaging for Improving the Accuracy of Cuffl...

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44th Annual international conference of the IEEE-engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Shen, Zhan Liu, Lei Ding, Xiaorong Univ Elect Sci & Technol China Sch Life Sci & Technol Chengdu Sichuan Peoples R China

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

In recent decades, many researches have proposed various models for continuous, cuffless blood pressure (BP) estimation. However, due to aleatoric uncertainty and epistemic uncertainty existing in the problem, it is very challenging to evaluate cuffless BP with acceptable accuracy. this paper innovatively proposes a cuffless BP ensemble estimation model based on Bayesian Model Average (BMA) method to reduce the epistemic uncertainty. We combine four most frequently cited physiological models and four regression models based on Photoplethysmogram (PPG) and Electrocardiogram (ECG) signals, and use the BMA method to assign weights to each model to achieve accurate cuffless BP prediction. the proposed method was validated on 17 healthy and 13 hypertensive subjects with continuous Finometer BP as a reference. the results showed that the error mean +/- SD (standard deviations) of both SBP and DBP predicted by the proposed method were 2.13 +/- 5.68 mmHg and 1.42 +/- 5.11 mmHg, respectively, which were both lower than each of the model. And the MAE was 6% and 8% lower than the best member of the model ensemble. We also analyzed the relationship between the number of training epochs and model prediction performance. When 15 cardiac cycles were choosed for training, it could get a good balance between model prediction accuracy and algorithm complexity. therefore, the proposed BMA method can solve the model uncertainty problem, providing robust and deterministic BP prediction.

关键词： Training Uncertainty Estimation predictive models Prediction algorithms Blood pressure Bayes methods

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7th IFIP TC 12 international conference on Computational Intelligence in Data Science, ICCIDS 2024

7th IFIP TC 12 International Conference on Computational Int...

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7th IFIP TC 12 international conference on Computational Intelligence in Data Science, ICCIDS 2024

ISBN: (纸本)9783031699818

the proceedings contain 72 papers. the special focus in this conference is on Computational Intelligence in Data Science. the topics include: Chat Bot in Banking Sector Using Machine Learning and Natural Language Processing;lecter-A Large Language Model Chatbot for Cognitive Behavioral therapy;Evaluating the Language Translation Accuracy of GPT-3.5 Using Prompt engineering;multi-camera Enhanced Real-Time Content-Aware Vehicle Detection;COOL: Classification of Online Offensive Language Using Machine Learning and Deep Learning;improved Evaluator for Subjective Answers Using Natural Language Processing;Self-harm Detection from Texts: A Comparative Study Utilizing BERT, Machine Learning, and Deep Learning Approaches;neuro-Evolution-Based Language Model for Text Generation;offensive Language Detection on Telugu Language;User Story Based Automated Test Case Generation Using NLP;anticipating Future College Admission Cutoffs: An Innovative predictive Model Incorporating Student Reviews and Historical Admissions Cutoff Data Using Machine Learning;sentiment Analysis for Stock Prediction Using Mass Media Sources;CADFRA: Coronary Artery Disease Feature Reduction with Autoencoder for Optimistic and Effective Classification;machine Learning Based Alzheimer’s Disease Detection: A Comprehensive Approach;supervised Learning of Procedures from Tutorial Videos;De-noising of Low Dose CT Liver Images Using Improved Discrete Wavelet Transform;a-Eye Tracker: Human Eye Defect Tracker and Analyzing software;design and Analysis of Structural Health Monitoring System for the Diagnosis of Morphological Deformities of Bolted Structures;Empowering Medical Image Analysis: Unveiling Anomalies through GANs and BiGAN’s models;pneumonia Detection Using Chest X-Rays: A Comprehensive Review;realistic Avatar Control through Video-Driven Animation for Augmented Reality;CNN-Based Skin Lesion Classification for Melanoma Detection.

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