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Heart Failure: Machine Learning Prediction Within a 5-Year Framework 24

Heart Failure: Machine Learning Prediction Within a 5-Year F...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Tsarapatsani, Konstantina-Helen Tsakanikas, Vasilios D. Sakellarios, Antonis Trampisch, Hans J. Karapintzou, Efterpi Rudolf, Henrik Matsopoulos, George K. Fotiadis, Dimitrios I. Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of Ioannina and Biomedical Research Institute-FORTH Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Ioannina Greece

ISBN: (纸本)9798331518622

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction. © 2024 IEEE.

关键词： Risk perception

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An Advanced Predictive Algorithm for Nanoparticle-Modified Resistive Ammonia Gas Sensor 12

An Advanced Predictive Algorithm for Nanoparticle-Modified R...

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12th International Conference on Awareness Science and Technology, iCAST 2023

作者： Lin, Kun-Wei Zheng, Cheng-You Liu, I-Ping Chen, Po-Lin dept. Computer Science and Information Engineering Chaoyang University of Technology Taichung Taiwan dept. Material Technology Green Technology Research Institute CPC Corporation Kaohsiung City Taiwan dept. Electrical Engineering National Cheng-Kung University Tainan Taiwan

ISBN: (纸本)9798350324693

Ammonia is currently widely used, but it can have adverse effects on human health. Therefore, the establishment of a highly sensitive and fast-responding ammonia sensor is necessary. A high-sensitivity nanoparticle-modified resistive ammonia sensor has been successfully fabricated. The resistance of the sensor decreases with increasing ammonia concentration and increases with increasing temperature. On the based of the measured current values and its trend, the predicted current values within the operating temperature and concentration range using linear regression analysis can be obtained. Additionally, another set of current values was estimated using the exponential function method based on the sensitivity values. By integrating the concept of the Kalman algorithm and the physical sensing mechanism of the device, we derived an advanced predictive current algorithm tailored specifically for sensor applications. This research has demonstrated a significant reduction in the measurement time and a substantial decrease in the gas consumption to ammonia sensing. As mentioned above, our study provides a new approach for sensor applications and model development. © 2023 IEEE.

关键词： Nanoparticles

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An Architectural Approach for Enhanced Data Interoperability Across Building systems 7

An Architectural Approach for Enhanced Data Interoperability...

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7th Conference on Cloud and Internet of Things, CIoT 2024

作者： Yefi, Peter Ejaz, Sikandar Menon, Ramanunni Parakkal Eicker, Ursula Guéhéneuc, Yann-Gaël Concordia University Computer Science and Software Engineering Dept. Montreal Canada Concordia University Institute for Information Systems Engineering Montreal Canada Institute for Solar Research German Aerospace Center Jülich Germany Concorida University NextGen Cities Institute Montreal Canada

ISBN: (纸本)9798350356717

Access to building data is crucial for creating portable applications and improving building operation and energy efficiency. Ensuring data transmutability is essential for facilitating research and overcoming the diverse data representation, management, and collection methods across various building management systems (BMSs). Different BMSs use multiple interfaces for data exchange. Some BMSs provide Application Programming Interfaces (APIs) for data exchanges, while others have gateways connecting to cloud services for application subscription and data access. This study introduces a five-layered architecture that describes how client applications, researchers, and other interested stakeholders can exchange data with different BMSs. Our objective is to help researchers and practitioners understand the various ways of accessing transmutable (capable of being transformed into a compatible format) data from BMSs, regardless of the building management system a building uses. Through transmutable access to building systems' data, we aim to enable transferable energy-efficient-related applications for buildings. We evaluate our work with a building model of the Varennes Library in Varennes, QC, Canada. The building model includes information about the building, floors, rooms, electric meter and historical time series data of the meter, weather station and its historical time series data. The model also has carbon dioxide concentration sensors, temperature sensors, humidity sensors, and historical time series data of the sensors. We include two test client programs to show how they access the transmutable data of the building through a model. We conclude that the five-layered architecture facilitates the exchange of transmutable data across diverse BMSs, making it a valuable tool for researchers and practitioners. © 2024 IEEE.

关键词： Middleware

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A Comparative Analysis of Two Dielectric Nanostructures to Enhance Efficiency of Perovskite Solar Cells

A Comparative Analysis of Two Dielectric Nanostructures to E...

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2022 International Conference on Advancement in electrical and Electronic engineering, ICAEEE 2022

作者： Hasen, Nayem Rahman, Aminur Singha, Showmik Chowdhury, Tahmid Aziz Shahjalal University of Science and Technology Dept. of Electrical and Electronic Engineering Sylhet3114 Bangladesh Rensselaer Polytechnic Institute Dept. of Electrical Computer and Systems Engineering TroyNY12180 United States

ISBN: (数字)9781665469449

ISBN: (纸本)9781665469449

Perovskite (CH3NH3PbI3) solar cells (PSCs) have recently been invented due to their desirable characteristics such as high absorption, low cost, ease of fabrication, and rapidly improving efficiencies. In recent years, the effectiveness of organometallic halide perovskite as an absorber layer has already surpassed 20%. Dielectric nanostructures are being considered as a possible option for third generation photovoltaics, such as PSCs, due to lower losses and simpler fabrication. Dielectric nanostructured antireflection coatings, on the other hand, have recently emerged as a method for reducing reflection and improving the power conversion efficiency (PCE) of a thin-film absorber that was previously inefficient. In this research, two different types of nanostructures are used on the perovskite absorber layer that reduced light reflection and enhanced the power absorption, short-circuit current, and efficiency of the solar cells. Furthermore, as demonstrated how changing the nanostructure size has an effect on the absorption enhancement. Utilizing the finite difference time domain (FDTD) and coupled optical-electrical models, the proposed optimum nanostructures exhibit greater than 90% power absorption, with the maximum enhanced photocurrent. Total photocurrent increased by 17-20% compared to conventional planar PSCs, with a maximum PCE of 16.78%, representing 37% enhancement throughout the entire wavelength range. © 2022 IEEE.

关键词： Perovskite

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Anomaly Detection With Quantified Explanations for Multivariate Time Series Networking Data

Anomaly Detection With Quantified Explanations for Multivari...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Marantis, Panagiotis Ramantas, Kostas Verikoukis, Christos Iquadrat Informatica S.L Barcelona Spain University of Patras Dept. of Computer Engineering and Informatics Patras Greece Athena Research Center Athens Greece Industrial Systems Institute Patras Greece

ISBN: (纸本)9798350351255

Next-generation telecommunication networks provide vast amounts of Multivariate Time Series monitoring data during their operation. Using these data to automatically detect anomalous system behavior is of critical importance to operators and Internet Service Providers, in order to analyze the network state and take mitigating actions to resolve possible faults. For the output of this Anomaly Detection process to be trusted, comprehensive and accurate explanations must be provided. These explanations are essential to make critical decisions that affect the network state. In this paper, we present a framework that utilizes reconstruction-based Deep Neural Network models to perform the anomaly detection task, while also using an explainability module that provides explanations of the results. The quality of the explanations is quantified through the use of interpretation labels. In addition, we provide a comparative analysis, performing Anomaly Detection on three different open-source datasets from the mobile networking domain, while testing multiple Deep Learning models and comparing their performance. The presented results show that our framework accurately detects various types of anomalies that can be present in mobile networks, while offering the flexibility to identify and select the model that offers the best performance for each dataset, in both the Anomaly Detection and explainability tasks. © 2024 IEEE.

关键词： DNNs Explainable Anomaly Detection Machine Learning Multivariate Time Series Next-Generation Networks Reconstruction-based Models

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Evaluation of Pseudorandom Pixel Placement in Enhancing Face Recognition using VGG-Face 5

Evaluation of Pseudorandom Pixel Placement in Enhancing Face...

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5th International Conference on Image, Video Processing, and Artificial Intelligence, IVPAI 2023

作者： Hadis, Muhammad Sabirin Akita, Junichi Toda, Masashi Dept. of Electrical Engineering and Computer Science Kanazawa University Kakuma-machi Ishikawa Kanazawa920-1192 Japan Research and Education Institute for Semiconductors and Informatics Kumamoto University Kumamoto860-8555 Japan

ISBN: (纸本)9781510674660

In efforts to enhance face recognition performance, techniques ranging from super-resolution methods to the use of Local Binary Pattern (LBP) and deep learning have been explored. Among these, the pseudorandom pixel placement (PSE) technique has demonstrated potential in face recognition. Nevertheless, its testing was previously limited to just 8 subjects. This study undertakes a comprehensive evaluation of the PSE technique with a larger sample, utilizing 2000 subjects from the DigiFACE1M dataset and leveraging the state-of-the-art VGG-Face deep learning model. Through experiments involving 10 different PSE patterns on 144.000 face images, our findings indicate that, compared to Regular Pixel Placement (REG), PSE achieved an improvement in average accuracy by 1.05%, reduced the standard deviation by 1.47%, and resulted in 31 additional subjects achieving 100% accuracy. We conclude that PSE consistently outperforms REG in face recognition tasks using the VGG-Face model across the majority of tested scenarios. © 2024 SPIE. All rights reserved.

关键词： Pixels

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Detection of False Data Injection Attacks in Smart Microgrid using Deep Learning to Improve Classification Algorithms

Detection of False Data Injection Attacks in Smart Microgrid...

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2023 International Conference on Smart-Green Technology in electrical and Information systems, ICSGTEIS 2023

作者： Shahinzadeh, Hossein Gharehpetian, Gevork B. Yaici, Wahiba Afzoon, Fatemeh Karimi, Ali Ebrahimi, Farshad Amirkabir University of Technology Department of Electrical Engineering Tehran Iran Natural Resources Canada CanmetENERGY Research Centre Ottawa Canada K. N. Toosi University of Technology Department of Electrical Engineering Tehran Iran Georgia Institute of Technology Dept. of Electrical and Computer Engineering AtlantaGA United States University of Houston Dept. of Electrical and Computer Engineering HoustonTX77004 United States

ISBN: (纸本)9798350382822

Detecting false data injection (FDI) attacks poses a significant challenge in smart microgrids. Recently, classification algorithms have been employed to address this issue. However, the effectiveness of these methods is hindered by the similarity between normal states and attack states. Consequently, the classification algorithm's accuracy is low, leading to undetected attacks. In the past few years, deep learning has emerged as a powerful approach for modeling complex nonlinear relationships and enhancing accuracy in various domains. In this study, we leverage a deep neural network (DNN) to improve the accuracy of classification algorithms in FDI attack detection. To accomplish this, we first employ the DNN to assign a value to each sample in the dataset, which consists of N features. This value is then added as an additional feature, resulting in a dataset with 1+N features. Subsequently, the augmented dataset is utilized for training and testing the desired classification algorithm instead of the original dataset. Experimental results demonstrate that augmenting the dataset with the feature obtained from the DNN significantly enhances the accuracy of classification algorithms in detecting FDI attacks. © 2023 IEEE.

关键词： Deep neural networks

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FORTUNATE: Decrypting and Classifying Malware by Variable Length Instruction Sequences 13

FORTUNATE: Decrypting and Classifying Malware by Variable Le...

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13th IEEE International Conference on Cloud Networking, CloudNet 2024

作者： Andrade, Cesar A. B. Rocha Filho, Geraldo P. Meneguette, Rodolfo I. Maranhao, Joao Paulo A. Sant'ana, Ricardo Duarte, Julio Cesar Serrano, Andre Luiz M. Goncalves, Vinicius P. University of Brasília Electrical Engineering Dept. Brasília Brazil State University of Southwest Bahia Exact and Technological Sciences Dept. Vitória da Conquista Brazil Institute of Mathematics and Computer Science Universityof São Paulo São Carlos Brazil Brazilian Army Systems Development Center Brasília Brazil Military Institute of Engineering Defense Engineering Dept. Rio de Janeiro Brazil Military Institute of Engineering Computing Engineering Dept. Rio de Janeiro Brazil University of Brasília Production Engineering Dept. Brasília Brazil

ISBN: (纸本)9798350376562

After identifying an artifact as malware, the additional challenge arises of correctly classifying it into a specific family. This raises the challenge of classifying malware amid growing complexity and the increasing volume of cyber threats. Therefore, we introduce FORTUNATE, a framework that employs variable-length instruction sequences to classify malware more efficiently and accurately, focusing on real and active malware. By introducing effective methods for extracting opcodes and their representation in the smallest possible vectors, FORTUNATE surpasses the limitations of previous approaches, enabling the correct identification of malware in their respective families, while avoiding redundancies in the data. The results achieved with FORTUNATE demonstrate advances in both precision and computational efficiency, consolidating it as a significant contribution to the field of cybersecurity. © 2024 IEEE.

关键词： Long short-term memory

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Human Detection Combining RGB and Thermal Modalities

Human Detection Combining RGB and Thermal Modalities

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Intelligent Reality (ICIR), IEEE International Conference on

作者： Chaymaa Fakrane Kennedy O. S. Mota Cristiano Premebida Rui P. Rocha Dept. of Electrical and Computer Engineering (DEEC) Institute of Systems and Robotics (ISR) University of Coimbra Portugal

ISBN: (数字)9798331534424

ISBN: (纸本)9798331534431

This paper addresses a late-fusion approach that combines RGB and thermal (long-wave IR) modalities to enhance human detection in indoor environments for robotic perception domains. The study explores multimodal human detection using YOLO series (v5s-6s and v8s-11s models), demonstrating significant improvements in detection accuracy under challenging lighting conditions on a dataset collected by a mobile robot. The use of a Weighted-Mean (WM) as a late-fusion approach demonstrates favorable results over more complex methods such as neural networks or SVM. The WM approach is simple to implement and has a low computational cost. Additionally, it is more interpretable and modular and allows straightforward adjustments to the weights of the YOLO models and thresholds, facilitating adaptation to the target system's specific needs. The experimental results indicate that the implemented late-fusion approach improves the baseline performance in terms of mean average precision.

关键词： YOLO Support vector machines Adaptation models Accuracy Computational modeling Neural networks Lighting Indoor environment Computational efficiency Mobile robots

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Protection of Inverter Interfaced Microgrids: Challenges and Solutions

Protection of Inverter Interfaced Microgrids: Challenges and...

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2022 IEEE/PES Transmission and Distribution Conference and Exposition, T and D 2022

作者： Dehghanpour, Ehsan Normandeau, Michel Joos, Geza McGill University Dept. Electrical and Computer Engineering Montreal Canada Hydro-Quebec Research Institute Montreal Canada

ISBN: (纸本)9781665443296

Recently, the protection of Inverter-Based Microgrids (IBMs) has attracted a lot of attention. It is partly because the penetration level of Inverter-Based Recourses (IBRs) is increasing continually and partly because of different and sometimes unusual behavior of these resources under fault conditions. The distinctive fault characteristic of IBRs makes the protection of the inverter-based microgrids rather uniquely challenging. This paper tries to shed light on the current challenges in the protection of the IBMs by providing time-domain simulations. Two possible solutions to address some of the main challenges are also discussed by providing simulation-based results using MATLAB Simulink software. © 2022 IEEE.

关键词： Fault detection

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