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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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Deep Equilibrium Models Meet Federated Learning

arXiv

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arXiv 2023年

作者： Gkillas, Alexandros Ampeliotis, Dimitris Berberidis, Kostas Dept. of Computer Engineering and Informatics University of Patras Greece Dept. of Digital Media and Communication Ionian University Greece

In this study the problem of Federated Learning (FL) is explored under a new perspective by utilizing the Deep Equilibrium (DEQ) models instead of conventional deep learning networks. We claim that incorporating DEQ models into the federated learning framework naturally addresses several open problems in FL, such as the communication overhead due to the sharing large models and the ability to incorporate heterogeneous edge devices with significantly different computation capabilities. Additionally, a weighted average fusion rule is proposed at the server-side of the FL framework to account for the different qualities of models from heterogeneous edge devices. To the best of our knowledge, this study is the first to establish a connection between DEQ models and federated learning, contributing to the development of an efficient and effective FL framework. Finally, promising initial experimental results are presented, demonstrating the potential of this approach in addressing challenges of FL. © 2023, CC BY.

关键词： Deep learning

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A System Architecture for Smart Farming on Mushroom Cultivation 5

A System Architecture for Smart Farming on Mushroom Cultivat...

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5th World Symposium on Communication engineering, WSCE 2022

作者： Moysiadis, Vasileios Karaiskou, Chrysoula Kokkonis, Georgios Moscholios, Ioannis D. Sarigiannidis, Panagiotis University of Western Macedonia Dept. of Electrical and Computer Engineering Kozani Greece University of Western Macedonia Dept. of Business Administration Grevena Greece University of Peloponnese Dept. of Informatics and Telecommunications Tripolis Greece

ISBN: (数字)9781665450577

ISBN: (纸本)9781665450577

Collecting data for various parameters in the agriculture domain aims to help farmers and agronomists to take proper decisions regarding the health and the status of the cultivation. Mushrooms are becoming one of the most valuable ingredients in nowadays diet, giving additional vitamins and flavour without sodium or fat. Moreover, various mushroom species can grow in greenhouses under restricted controlled conditions. Furthermore, atmospheric parameters in the greenhouse and parameters from the substrate are of paramount importance in mushroom cultivation. Therefore, measuring and controlling these parameters using modern approaches based on Internet of Things technologies might leverage production. In addition, a decision support system based on aggregated data can help mushroom farmers in everyday decisions. In this manuscript, we propose a system architecture for data acquisition and decision-making for a greenhouse with mushrooms based on innovative technologies. Besides, we provide directions for wild mushroom hunting based on UAVs and sensors deployed in forests. © 2022 IEEE.

关键词： Greenhouses

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On a Piecewise Linear Function Approximation for Quantum Computation

On a Piecewise Linear Function Approximation for Quantum Com...

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IEEE Asia-Pacific Conference on Circuits and Systems

作者： Hideaki Okazaki Dept. of Informatics Faculty of Informatics Computer Science Course (or Field) Graduate school of Electrical and Information Engineering Shonan Institute of Technology Fujisawa Kanagawa Japan

In quantum computing, although correspondences between quantum circuits and polynomials have been explored, a correspondence between a quantum circuit and a low-degree polynomial, even a linear function has not fully clarified. In this paper, a quantum circuit approximating a linear function for building piecewise linear functions, is proposed. First, for qubit operations, a dimensionless form in $[0,1]$ of a linear function is provided. Secondly, a theorem of a parameterized quantum circuit approximating linear functions are provided. Thirdly, by using Yao: an open source Julia package, the quantum circuit simulation results to approximate the linear functions are presented. Finally, the principal results are summarized.

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DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clustering and Deep Learning for Scalable Indoor Localization Using Wi-Fi RSSI

DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clusterin...

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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Muhammad Izzuddin Mahali Jenq-Shiou Leu Nur Achmad Sulistyo Putro Setya Widyawan Prakosa Cries Avian Dept. of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Dept. of Electronics and Informatics Engineering Education Universitas Negeri Yogyakarta Yogyakarta Indonesia Dept. of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

This research introduces a novel method for indoor positioning by integrating Fuzzy C-Means clustering with deep learning autoencoders, leveraging Wi-Fi RSSI fingerprinting for precise location prediction and building and floor level classification within a unified framework. The approach combines the comprehensibility of Fuzzy C-Means (FCM) with the robust feature extraction capabilities of deep autoencoders through a dual-phase strategy: extensive offline training utilizing RSSI fingerprint data followed by real-time online position prediction. The model's performance was rigorously tested using 5-Fold cross-validation, examining its effectiveness both with and without the integration of FCM on various cluster numbers. Evaluation metrics, including Euclidean distance error for regression and accuracy, F1-score, precision, and specificity for classification effectiveness, were used for comparative analysis. The results were visualized through regression plots, confusion matrices, and learning curves, supplemented by t-SNE visualizations that provide profound insights into the feature space. These detailed analyses highlight the considerable promise of this integrated approach in enhancing the accuracy and reliability of indoor positioning systems.

关键词： Location awareness Deep learning Training Measurement Accuracy Clustering algorithms Fingerprint recognition Real-time systems Reliability Wireless fidelity

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An Implementation of Automatic Dart Code Verification for Mobile Application Programming Learning Assistance System Using Flutter

An Implementation of Automatic Dart Code Verification for Mo...

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2022 International Conference on Electrical and Information Technology, IEIT 2022

作者： Syaifudin, Yan Watequlis Hatjrianto, Agus Salim Funabiki, Nobuo Liliana, Dewi Yanti Kaswar, Andi Baso Nurhasan, Usman Dept. of Information Technology State Polytechnic of Malang Malang Indonesia Dept. of Elec. and Comm. Engineering Okayama University Okayama Japan Dept. of Informatics Engineering State Polytechnic of Jakarta Jakarta Indonesia Dept. of Computer Engineering State University of Makassar Makassar Indonesia

ISBN: (数字)9781665453035

ISBN: (纸本)9781665453035

The popularity of smartphone devices has rapidly increased in recent years and many people utilize smartphones for various needs. The development of mobile applications has been aimed at various fields that make the demand for mobile application programmers increase. Recently, Flutter has become a software development kit for cross-platform applications development, including Android and iOS, so many software developers have adopted it. To provide a self-learning system for studying mobile programming with Flutter, we propose a learning assistance system with an automatic Dart code verification feature. Based on our previous studies in Android Programming Learning Assistance System (APLAS), the automatic code verification process can adopt software testing process for Android applications. The learning model provides learning materials for studying and practicing by solving an assignment. A learning topic of developing a simple application is prepared for the proposed system evaluation. 40 university students in IT department have been appointed to study Flutter and solve the assignment. Finally, they can solve the assignment correctly and give positive opinions about using this system. © 2022 IEEE.

关键词： Smartphones

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Quantum Data Reduction with Application to Video Classification

Quantum Data Reduction with Application to Video Classificat...

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IEEE/ACM Symposium on Edge Computing (SEC)

作者： Kostas Blekos Dimitrios Kosmopoulos Computer Engineering & Informatics Dept. University of Patras Rio Greece

ISBN: (数字)9781665486118

ISBN: (纸本)9781665486125

We investigate a quantum data reduction technique with application to video classification. A hybrid quantum-classical step performs data reduction on the video dataset generating “representative” distributions for each video class. These distributions are used by a quantum classification algorithm to firstly reduce the size of the videos and then classify the reduced videos to one of $k$ classes. We verify the method using sign videos and demonstrate that the reduced videos contain enough information to successfully classify them using a quantum classification process. The proposed data reduction method showcases a way to alleviate the “data loading” problem of quantum computers for the problem of video classification. Data loading is a huge bottleneck, as there are no known efficient techniques to perform that task without sacrificing many of the benefits of quantum computing.

关键词： computers Quantum computing Loading Hybrid power systems Classification algorithms Task analysis Edge computing

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A 2-D Wrist Motion Based Sign Language Video Summarization 32

A 2-D Wrist Motion Based Sign Language Video Summarization

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32nd British Machine Vision Conference, BMVC 2021

作者： Sartinas, Evangelos G. Psarakis, Emmanouil Z. Antzakas, Klimis Kosmopoulos, Dimitrios I. Dept. of Computer Engineering & Informatics University of Patras Greece Dept. of Primary Education University of Patras Greece

In this paper we present a keyframe extraction scheme based on the wrist motion using differential geometry. More specifically, the time (t)-parameterized Frennet-Serret frame for tracking the signer's wrist is used and the curvature of the trajectory, is proposed for the identification of the Sign Language (SL) video keyframes. Specifically, a video frame is characterized as keyframe if on that time instance the t-parameterized curvature function attains a maximum value. Finally, in order to properly define the wrist 2-D motion model, a skeleton tracker is used. The proposed scheme is adaptable, i.e., the number of extracted keyframes varies according to the complexity of the signs, while preserving the semantic content. This in turn makes it attractive for applications like video-calling. Its performance in terms of the achieved compression and intelligibility ratios was evaluated on a ground-truth sequence and outperformed its s-parameterized counterpart (s is the arc length);it also outperformed a moment-based SL summarization technique. Furthermore, the proposed scheme was experimentally evaluated on a dataset containing 5500 signs by SL specialists with very promising results. Finally, the proposed keyframe extraction was evaluated against the aforementioned techniques on the same dataset via the use of a GRU neural network on the gloss classification problem;its superior accuracy in identifying the gloss meaning was confirmed. © 2021. The copyright of this document resides with its authors.

关键词： Classification (of information)

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Drone-to-drone Collision Detection Algorithm in Flight Traffic Management

Drone-to-drone Collision Detection Algorithm in Flight Traff...

引用

International Electronics Symposium (IES)

作者： Muhajirin Ida Ilyas Idris Winarno Dadet Pramadihanto Dept. of Information and Computer Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Dept. of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia

This paper presents an algorithm for detecting collisions in flight traffic management for drones. The algorithm is simple to implement and can be used to effectively manage the flow of drones in a safe and efficient manner. The algorithm has been tested in simulation and has been shown to be effective in detecting collisions between drones. The algorithm is also simple to implement and can be used with existing drone traffic management systems. As the number of drones in the sky continues to grow, this algorithm will become increasingly important for ensuring the safety of drone operations.

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Sentiment Analysis Twitter (X) Users Towards the Boycott of Pro-Israel Brand Using Support Vector Machine Method with SMOTE Approach

Sentiment Analysis Twitter (X) Users Towards the Boycott of ...

引用

Control & Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence (CERIA), IEEE International Conference on

作者： Hana Patricia Mawarnie Widodo Murien Nugraheni Dept. of Informatics and Computer Engineering Education Universitas Negeri Jakarta Jakarta Indonesia Dept. of Information System and Technology Universitas Negeri Jakarta Jakarta Indonesia

ISBN: (数字)9798331511166

ISBN: (纸本)9798331511173

The Israel-Palestine conflict that occurred on October 7, 2023, resulted in a boycott of brands known to be pro-Israel. Twitter or X, as one of the largest social media platforms in the world, became a platform for people to voice their opinions regarding the boycott. This study aims to analyze the sentiment of Twitter or X users towards the boycott of pro-Israel brands using the Support Vector Machine (SVM) method with the SMOTE (Synthetic Minority Oversampling Technique) approach. The Support Vector Machine (SVM) method is a machine learning method used for classification and regression. SMOTE is a technique used to overcome data imbalance. The data used in this study is Indonesian language tweet data from October 7, 2023, to April 7, 2024. The data was collected using data crawling techniques. The results of the study from the 80:20 data split, show that the Support Vector Machine (SVM) polynomial kernel model with the SMOTE technique has better performance in analyzing the sentiment of Twitter or X users towards the boycott of pro-Israel brands, with an accuracy value of 98,47%.

关键词： Support vector machines Sentiment analysis Accuracy Social networking (online) Blogs Machine learning Polynomials Internet of Things Kernel Robots

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