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PheME: A deep ensemble framework for improving phenotype prediction from multi-modal data 11

PheME: A deep ensemble framework for improving phenotype pre...

引用

11th IEEE international conference on Healthcare Informatics, ICHI 2023

作者： Zhang, Shenghan Li, Haoxuan Tang, Ruixiang Ding, Sirui Rasmy, Laila Zhi, Degui Zou, Na Hu, Xia Xi'an Jiaotong-Liverpool University Department of Computer Science Xi'An China Wuhan University Department of Computer Science Wuhan China Rice University Department of Computer Science Houston United States Texas A&m University Department of Computer Science and Engineering College Station United States UTHealth Houston School of Biomedical Informatics Houston United States Texas A&m Univerisity Department of Engineering Technology & Industrial Distribution College Station United States

ISBN: (纸本)9798350302639

Detailed phenotype information is fundamental to accurate diagnosis and risk estimation of diseases. As a rich source of phenotype information, electronic health records (EHRs) promise to empower diagnostic variant interpretation. However, how to accurately and efficiently extract phenotypes from the heterogeneous EHR data remains a challenge. In this work, we present PheME, an Ensemble framework using Multi-modality data of structured EHRs and unstructured clinical notes for accurate Phenotype prediction. Firstly, we employ multiple deep neural networks to learn reliable representations from the sparse structured EHR data and redundant clinical notes. A multi-modal model then aligns multi-modal features onto the same latent space to predict phenotypes. Secondly, we leverage ensemble learning to combine outputs from single-modal models and multi-modal models to improve phenotype predictions. We chose seven diseases to evaluate the phenotyping performance of the proposed framework. Experimental results show that using multi-modal data significantly improves phenotype prediction in all diseases, the proposed ensemble learning framework can further boost the performance. © 2023 IEEE.

关键词： Forecasting

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Phonocardiographic Signal Analysis for the Detection of Cardiovascular Diseases 11th

Phonocardiographic Signal Analysis for the Detection of Card...

引用

11th international conference on Frontiers of intelligent Computing: theory and Applications, FICTA 2023

作者： Gupta, Deena Nath Anand, Rohit Ahamad, Shahanawaj Patil, Trupti Dhabliya, Dharmesh Gupta, Ankur CDAC Maharashtra Mumbai India New Delhi110020 India College of Computer Science and Engineering University of Hail Hail City Saudi Arabia Department of Engineering and Technology Bharati Vidyapeeth Deemed to be University Maharashtra Navi Mumbai India Department of Information Technology Vishwakarma Institute of Information Technology Maharashtra Pune India Department of Computer Science and Engineering Vaish College of Engineering Haryana Rohtak India

ISBN: (纸本)9789819967056

Under this work, we propose a novel method for analyzing heart sounds to find cardiovascular illnesses. the phonocardiography (PCG) signal is divided into four pieces by the heart sound segmentation process: S1, systolic interval, S2, and diastolic interval. One may argue that it is an essential stage in the automated analysis of PCG signals. Mechanical activity of the circulatory system is conveyed via heart sounds. this data comprises the subject’s precise physiological condition as well as any short-term variations connected to the respiratory cycle. this paper’s focus is on an issue that is currently open: how to analyze noises and extract physiological state changes while keeping short-term variability. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Phonocardiography

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Detecting the Attention Span of Autistic Children 11

Detecting the Attention Span of Autistic Children

引用

11th international conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET - SIP 2023

作者： Kalbande, Dhanajay Rathod, Surendra Singh Oza, Riddhi Dinesh Dhamane, Shreyash Sardar Patel Institute of Technology Computer Science & Engineering Mumbai India Computer Engineering Fr. Conceicao Rodrigues College of Engineering Mumbai India

ISBN: (纸本)9798350348422

this study investigates methods for detecting and monitoring the attention span of autistic children in real-time, with the goal of improving learning outcomes. the study involved monitoring the movements and reactions of a group of autistic children during various activities, using specialized sensors and software to capture and analyze data related to their attention span. the results showed that the attention span of autistic children varied significantly across different activities and stimuli, and certain types of games and learning exercises were more effective at capturing and maintaining their attention than others. the attention span of autistic children also tended to improve gradually over time, particularly when they were engaged in activities that were interesting and stimulating. these findings have important implications for the design of effective interventions and learning activities for autistic children, and highlight the importance of personalized and evidence-based approaches for improving attention span and learning outcomes. © 2023 IEEE.

关键词： attention span autism real-time monitoring

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Application of Artificial Intelligence Techniques in Healthcare data Analytics 11th

Application of Artificial Intelligence Techniques in Healthc...

引用

11th international Work-conference on Bioinformatics and Biomedical engineering (IWBBIO)

作者： Gancheva, Veska Lazarova, Milena Vetova, Stella Sharabov, Maksim Tsochev, Georgi Tech Univ Sofia Fac Comp Syst & Technol 8 Kliment Ohridski Sofia 1000 Bulgaria

ISBN: (纸本)9783031646287;9783031646294

Technological developments have revolutionized biomedical research and treatment, resulting in the massive collection of diverse biomedical data. Artificial intelligence techniques are widely used by medical researchers because they make it possible to identify and create models of complex datasets and the relationships between them, which in turn allows for the successful prediction of future outcomes related to a particular illness type. A methodology for medical data analysis by applying artificial intelligence techniques is proposed, which uses data to construct a desired model and address a specific problem. the methodology involves utilizing data to construct the desired model and address a particular issue. the proposed approach for healthcare data analytics applies an algorithm based on machine learning to select the most suitable model. the preprocessing phase is associated with feature extraction, data function selection, data cleaning and selection of training and validation datasets. the input dataset is transformed into numerical format. A cardiovascular data analysis workflow applying a structured approach to develop a predictive model using neural network consisting of two hidden layers is designed.

关键词： Artificial Intelligence data Analytics Healthcare data

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Towards WebAssembly-Based Federated learning 11th

Towards WebAssembly-Based Federated Learning

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11th IFIP WG 6.12 European conference on Service-Oriented and Cloud Computing, ESOCC 2025

作者： Gottschalk, Felix Schulte, Stefan Hemadasa, Nisal Ebrahimi, Elmira Edinger, Janick Kaaser, Dominik Christian Doppler Laboratory for Blockchain Technologies for the Internet of Things Institute for Data Engineering TU Hamburg Hamburg Germany University of Hamburg Hamburg Germany

ISBN: (纸本)9783031846168

WebAssembly is a portable binary instruction format designed to serve as a compilation target for high-level languages. While originally developed to run performance-intensive applications directly in Web browsers, WebAssembly supports these days a number of different hardware platforms across the compute continuum. this makes it a promising option to run services for training and inference in Federated learning. To the best of our knowledge, there have been only a few practical approaches to realize Federated learning using WebAssembly. therefore, in this paper, we present a framework to achieve this. Our prototypical implementation shows that WebAssembly-based Federated learning applications are highly portable while providing acceptable runtime overhead during model training. © IFIP international Federation for Information Processing 2025.

关键词： Federated learning Machine learning WebAssembly

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Number of Road Accidents Predicting Using Deep learning-Based LSTM Development Models 11

Number of Road Accidents Predicting Using Deep Learning-Base...

引用

11th international conference on Cyber and IT Service Management, CITSM 2023

作者： Siswanto, Joko Hendry Rahardja, Untung Sembiring, Irwan Hartomo, Kristoko Dwi Purnomo, Hindriyanto Dwi Iriani, Ade Road Transportation System Engineering Politeknik Keselamatan Transportasi Jalan Tegal Indonesia Satya Wacana Christian University Faculty of Information Technology Salatiga Indonesia University of Raharja Faculty of Science and Technology Tangerang Indonesia

ISBN: (纸本)9798350305968

Road accidents cause chaos and state financial losses every year. Road accidents cannot be stopped but can be controlled with predictions. the existing road accident prediction model accuracy for the medium and long term still has many discrepancies, and it is rarely done. A comparison of the deep learning algorithms of the LSTM, Bi-LSTM, ConvLSTM, and Att-LSTM models with hidden layer variations is used to select the suitable model to make predictions. Use data from the road accident dataset in thailand in 2011-2022, which can be accessed freely on Kaggle. the results show that the BiLSTM model with 4 hidden layers can predict the number of road accidents in the medium and long term with the highest accuracy. MSLE is used to measure the accuracy of the model with variations without hidden layers, 2 hidden layers, and 4 hidden layers to find the best model for predicting the number of road accidents for the medium and long term. © 2023 IEEE.

关键词： Forecasting

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Reinforcement learning-Based Energy Management Algorithms Effect on Microgrid Physical Properties 11

Reinforcement Learning-Based Energy Management Algorithms Ef...

引用

11th IEEE international conference on Systems and Control, ICSC 2023

作者： Swibki, Taheni Salem, Ines Ben Kraiem, Youssef Amraoui, Lilia El Abbes, Dhaker National Engineering School of Carthage University of Carthage Charguia Ii Research Laboratory Smart Electricity & Ict Seict LR18ES44 Tunis Tunisia Univ. Lille Arts et Metiers Institute of Technology Centrale Lille Junia Ulr 2697 L2EP LilleF-59000 France

ISBN: (纸本)9798350304886

this paper studies the application of Artificial Intelligence (AI), precisely Reinforcement learning (RL), to minimize energy costs of a grid-connected community Microgrid (MG). this MG is composed of photovoltaic (PV) panels, dynamic and static loads, and a battery storage system (BSS). RL algorithms learn through interacting with a MG simulator to make decisions in real time that minimizes energy costs without prior knowledge of uncertainties related to PV production, load consumption and electricity costs. In this article, two RL algorithms, Q-learning and State-Action-Reward-State-Action (SARSA), are studied. these algorithms were benchmarked with two baselines in order to test its effectiveness. the first baseline involves no storage, and the second baseline involves storage management provided by a linear programming (LP) algorithm. the main contribution of this work is the investigation of the RL-based energy management approaches on the MG voltage magnitude at the Point of Common Coupling (PCC). In this sense, an electrical model of the studied MG is adopted. Simulation results shows effective results of the data-driven methods (based on Q-learning and SARSA) where no physical constraints violating have been recorded. © 2023 IEEE.

关键词： Reinforcement learning

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Big data Application in Aerospace Product intelligent Quality Control: A Survey 11th

Big Data Application in Aerospace Product Intelligent Qualit...

引用

11th international conference on Signal and Information Processing, Network and Computers, ICSINC 2023

作者： Meng, Lingtong Ji, Huifang Zheng, Xianglin Song, Huafeng Zhang, Jinglin Chu, Hongwei Beijing Institute of Control Engineering Beijing100090 China

ISBN: (纸本)9789819721153

A systematic approach to quality management has been accumulated in aerospace industry, and also a large amount of quality data has also been accumulated, making it possible to mine the potential value of aerospace quality big data. In this paper, we investigate the application of vast amounts of data in process monitoring, quality prediction, and product process optimization. About Aerospace products quality control, this paper proposes scenarios for applications ranging from the collection and storage of quality data to what can be applied in real-time decision making such as traceability analysis, and what can be used to prevent quality risks through data statistical analysis and related business experience analysis. Finally, taking a flywheel product as an example, the data envelope analysis method is successfully applied to analyze and apply the test data of the product, and it is proposed that knowledge precipitation can promote continuous quality improvement. this paper provides some ideas on how to complete big quality data collection, integration and ways to mine the value of aerospace quality data. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Process monitoring

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Integrating Deep learning Models and Depth Cameras to Achieve Digital Transformation: A Case Study in Shoe Company 30

Integrating Deep Learning Models and Depth Cameras to Achiev...

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30th ISTE international conference on Transdisciplinary engineering: Leveraging Transdisciplinary engineering in a Changing and Connected World, TE 2023

作者： Yang, Li-Sheng Chiu, Ming-Chuan Department of Industrial Engineering and Industrial Management National Tsing Hua University Taiwan

ISBN: (纸本)9781643684406

In today's fiercely competitive industrial environment, digital transformation and smart manufacturing have become important strategies for enhancing competitiveness. Digital transformation utilizes advanced technology and data analysis to make manufacturing processes more intelligent and automated, improve product quality, reduce production costs and time, and increase production efficiency. Smart manufacturing further applies machine learning, deep learning, and artificial intelligence to make the production process even more intelligent and automated. However, existing object detection models such as YOLO can only provide rectangular bounding boxes and cannot determine the actual rotation angle and inclination of objects, and lack discourse on hardware integration. therefore, this study proposes a deep learning-based method framework that combines Yolov5 and Mask R-CNN to detect objects in real-time and calculate the object's center point coordinates, reference point coordinates for rotation direction, and inclination angle. this is integrated with a depth camera to obtain the distance between the robotic arm and the object, providing all the information required for the robotic arm to grasp the object. In simulated scenarios of stacking shoe insoles, the model proposed in this study achieved an accuracy of 97%. this technology can be applied in the factory production process, allowing robotic arms to accurately grasp objects from cluttered piles at the correct coordinates and angles, and perform sorting and assembly tasks. It can also help companies reduce costs and errors caused by human intervention, thereby enhancing their competitiveness. © 2023 the Authors.

关键词： Production efficiency

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Optimizing Machine learning Models for Predicting User Engagement in Online learning Platforms: the Role of Feature Selection 11

Optimizing Machine Learning Models for Predicting User Engag...

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11th international conference on Computer, Control, Informatics and its Applications, IC3INA 2024

作者： A’la, Fiddin Yusfida Riasti, Berliana Kusuma Department of Informatics Engineering Universitas Sebelas Maret Surakarta Indonesia

ISBN: (纸本)9798331542313

Online education has emerged as a popular alternative to traditional learning environments, driven by its flexibility in terms of time, location, and pace. Understanding the factors that influence user engagement and course completion is essential for designing effective online learning experiences. this study explores how features such as time spent on courses, number of videos watched, quizzes taken, quiz scores, and device type impact engagement and course completion. Using a dataset incorporating these features, the Random Forest algorithm was employed to identify feature importance, highlighting its effectiveness in handling high-dimensional data. Additionally, the performance of five machine learning algorithms—Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), and Gradient Boosting Machines (XGBoost)—was compared. Results indicate that Random Forests and XGBoost consistently achieve high accuracy, demonstrating robustness to feature reduction. In contrast, Logistic Regression showed sensitivity to reduced feature sets, while Decision Trees and SVM exhibited moderate performance drops. the findings provide valuable insights into optimizing online learning platforms, enhancing student engagement, and reducing dropout rates by identifying key behavioral predictors and selecting suitable algorithms for predictive modeling. © 2024 IEEE.

关键词： Adversarial machine learning

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