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11th International Conference on Information Technology and Quantitative Management, ITQM 2024

作者： Roman, Raul-Cristian Precup, Radu-Emil Petriu, Emil M. Politehnica University of Timisoara Dept. Automation and Applied Informatics Bd. V. Parvan 2 Timisoara300223 Romania Center for Fundamental and Advanced Tech. Res. Romanian Academy - Timisoara Br. Bd. Mihai Viteazu 24 Timisoara300223 Romania University of Ottawa School of Electrical Engineering and Computer Science 800 King Eduard OttawaONK1N 6N5 Canada

Active Disturbance Rejection Control (ADRC) is a data-driven algorithm that offers a promising approach for robust control design. This paper investigates the effectiveness of first-order and second-order ADRC for 3D cranes. To compare the results fairly and reduce the heuristics in parameter tuning, the ADRC parameters were optimally computed via the African Vultures Optimization Algorithm. Both controllers based on ADRC are validated using experiments on a 3D crane laboratory system and their design and performance evaluation are done by minimizing the sum of squared errors. The results demonstrate the robustness of the ADRCs. This comparative analysis highlights the advantages and limitations of both control strategies, providing valuable insights for selecting the appropriate ADRC order for specific applications. © 2024 The Authors.

关键词： Disturbance rejection

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Referenceless User Controllable Semantic Image Synthesis

arXiv

引用

arXiv 2023年

作者： Kim, Jonghyun Li, Gen Kim, Joongkyu AI Lab CTO Division LG Electronics Seoul Korea Republic of School of Informatics University of Edinburgh Edinburgh United Kingdom Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

Despite recent progress in semantic image synthesis, complete control over image style remains a challenging problem. Existing methods require reference images to feed style information into semantic layouts, which indicates that the style is constrained by the given image. In this paper, we propose a model named RUCGAN for user controllable semantic image synthesis, which utilizes a singular color to represent the style of a specific semantic region. The proposed network achieves reference-free semantic image synthesis by injecting color as user-desired styles into each semantic layout, and is able to synthesize semantic images with unusual colors. Extensive experimental results on various challenging datasets show that the proposed method outperforms existing methods, and we further provide an interactive UI to demonstrate the advantage of our approach for style controllability. The codes and UI are available at: https://***/BenjaminJonghyun/RUCGAN © 2023, CC BY-NC-SA.

关键词： Color

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Brain tumor identification using fcm threshold method and morphological area selection

Brain tumor identification using fcm threshold method and mo...

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2020 International Seminar on Application for Technology of Information and Communication, iSemantic 2020

作者： Setyawan, Robert Asrori, Riris Bayu Fajar Shidik, Guruh Fanani, Ahmad Zainul Anggi Premunendar, Ricardus Dept. Informatics Engineering of Computer Science Faculty Dian Nuswantoro University Semarang Indonesia

ISBN: (数字)9781728190686

ISBN: (纸本)9781728190686

Brain tumors segmentation has become a popular research topic in the last five years, proved by the emergence of many methods proposed to segment brain tumors accurately. In this study, the authors propose a brain tumor segmentation method based on the FCM method with a modification of the threshold value, which will later be used to convert an MRI image to a binary image with only the tumor area detected. The segmentation process divided into three stages, with steps is preprocessing segmentation and post-processing. In the preprocessing stage, the skull bones from MRI images are removed, then the noise is removed using Wiener filters, then proceed with the segmentation stage using FCM Thresh, and finally applying morphological area selection to select areas from segmentation results. From a total of 100 positive tumor MRI images that we acquire from the BRATS 2015 dataset, we obtained an average similarity of 0.7592. We achieved an improvement of 0.06 in term of SSIM value from the previous method. © 2020 IEEE.

关键词： Magnetic resonance imaging

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Healthcare Industry 5.0: Pareto Optimal IoT-Based Health Monitoring With Federated Learning

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IEEE Internet of Things Journal 2025年

作者： Diamantoulaki, Ioanna Tegos, Sotiris A. Bouzinis, Pavlos S. Sarigiannidis, Panagiotis Chatzakis, Christos Petousis, Stamatios Maglaveras, Nicos Karagiannidis, George K. Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki54124 Greece Kila Kozani MetaMind Innovation P.C. Kozani50100 Greece University of Western Macedonia Department of Informatics and Telecommunications Engineering Kozani50100 Greece Aristotle University of Thessaloniki AUTH Campus Medical School 2nd Dept. of Obstetrics and Gynaecology Thessaloniki54636 Greece

With the recent expansion of patient data availability and storage capabilities, healthcare entities tend to store an increasing amount of medical data locally. In addition, ongoing advances in the era of healthcare industry 5.0 and Artificial Intelligence of Things can lead to more efficient use of medical data, resulting in better health monitoring at lower costs. However, due to strict privacy restrictions related to the sensitive nature of medical data, it is often only used locally and ultimately underutilized. To this end, federated learning (FL) offers a promising solution for the efficient use of medical data, facilitating the development of reliable and robust healthcare tools. This can be achieved thanks to its decentralized nature, which allows participating entities to collaborate and thus develop and train a centralized shared model without requiring data sharing. Taking this into account, we present a Pareto-front optimization framework for FL-based health monitoring that is able to mitigate false negative predictions for the required level of false positives. By applying the proposed framework to four different medical applications, it is shown that the risk of misdiagnosis is significantly reduced, providing an additional tool for medical professionals. © 2014 IEEE.

关键词： Federated learning

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Performance analysis graph-based keyphrase extraction in Indonesia scientific paper

Performance analysis graph-based keyphrase extraction in Ind...

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2020 International Seminar on Application for Technology of Information and Communication, iSemantic 2020

作者： Asrori, Riris Bayu Setyawan, Robert Muljono, Muljono Dept. Informatics Engineering of Computer Science Faculty Dian Nuswantoro University Semarang Indonesia

ISBN: (纸本)9781728190686

The increasing number of research papers from Indonesia and the higher the need to manage large amounts of documents, including memory capacity and the availability of time to manage research documents, keyphrase can provide a picture representing a text document. Thus, the management of documents in large quantities will become easier to prepare and find a collection of documents. For this reason, proper and correct keyphrase extraction can assist in managing paper documents. This study tried to compare three extraction methods using unsupervised graph-based techniques, including TextRank, TopicRank, and PositionRank. We compare the extraction results with the keyphrase made by the authors of the paper to see the three method's performance. From the results of our research, we found that the method TextRank Providing the highest performance compared to other methods using the ROUGE test with average Recall, Precision, and F1-Score are 0.558, 0.71, and 0.225. However, the extracted phrases are longer compared to others. The performance of TopicRank, although it does not provide higher results with TextRank, the extracted keyphrase is more compact than TextRank and PositionRank methods. © 2020 IEEE.

关键词： Extraction

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By Machine Learning Techniques Predicting Post-COVID-19 Condition

By Machine Learning Techniques Predicting Post-COVID-19 Cond...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Pei-Rong Huang Chih-Hung Chang Wen-Ching Chen Che-Lun Hung Po-Yu Liu Ting-Kuang Yeh Hsiu-Wen Wang Yu-Chun Yen William Cheng-Chung Chu Dept. of Computer Science and Communication Engineering Providence University Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taiwan Division of Infectious Diseases Taichung Veterans General Hospital Taiwan Clinical Information Department Quality Management Center Taichung Veterans General Hospital Dept. of Computer Science Tunghai University Taiwan

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

As the COVID-19 pandemic continues, a growing number of recovered patients report persistent symptoms such as fatigue, muscle weakness, sleep issues, anxiety, and depression, lasting months or even over a year. Severe cases often show significant lung damage and sometimes reduced kidney function. This study examines a dataset from recovered COVID-19 patients, using machine learning to assess the likelihood of developing Post-COVID-19 conditions. We applied several models, including XGBoost, Decision Trees, and Random Forest, to predict outcomes based on data from a specific hospital. Our approach included detailed data preprocessing-filling in missing values, feature engineering, and standardizing data to improve model accuracy and applicability. Results showed the Random Forest model as the most accurate, demonstrating the power of machine learning in making precise predictions from complex health data. Feature importance analysis revealed critical factors predicting Post-COVID-19 conditions, offering vital guidance for healthcare professionals in managing recovered patients.

关键词： COVID-19 Accuracy Pandemics Lung Predictive models Muscles Data models

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Advancing Fetal Surveillance with Physiological Sensing: Detecting Hypoxia in Fetal Sheep

Advancing Fetal Surveillance with Physiological Sensing: Det...

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IEEE SENSORS

作者： Weitao Tang Nhi Tran Nasim Katebi Reza Sameni Gari D. Clifford David Walker Vaishnavi Horlali Callum Taylor Robert Galinsky Faezeh Marzbanrad Electrical and Computer System Engineering Monash University Melbourne Australia The Ritchie Centre Hudson Institute of Medical Research Melbourne Australia Dept. of Biomedical Informatics Emory University Atlanta USA Dept. of Biomedical Engineering Georgia Institute of Technology Emory University Atlanta USA Department of Obstetrics and Gynaecology Monash University Melbourne Australia

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

Accurate detection of hypoxia during fetal monitoring is critical for timely intervention and prevention of brain injury. This study investigated hypoxia detection in fetal sheep by developing a classifier to differentiate between baseline, during acute hypoxia (umbilical cord occlusion; UCO), and post-hypoxia recovery (post-UCO), using physiological signals and an XGBoost machine learning model. A multi-modal approach integrating electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG) signals was compared to a model based on the ECG signal only. We included recordings from 14 fetal sheep, each of which underwent acute hypoxia via a single UCO procedure. Performance of hypoxia detection was measured by Marco F1 score (F1 m ), Accuracy (Acc), and standard deviation of validation error (SDE). Our multi-modal model achieved F1 m = 0.88, Acc = 0.90, while the ECG-based approach achieved F1 m = 0.82, Acc = 0.85. These results indicate that the multi-modal approach was superior to the ECG-only model for overall classification. While ECG and multimodal models correctly classified over 92% and 97% of acute hypoxia intervals, respectively, the classification of post- UCO intervals proved more challenging. The complexity of interpreting physiological signals underscores the need to consider temporal dynamics and physiological context in fetal monitoring.

关键词： Accuracy Machine learning Electrocardiography Hypoxia Brain modeling Feature extraction Electromyography Electroencephalography Complexity theory Biomedical monitoring

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Hierarchical Context Transformer for Multi-level Semantic Scene Understanding

arXiv

引用

arXiv 2025年

作者： Hao, Luoying Hu, Yan Yue, Yang Wu, Li Fu, Huazhu Duan, Jinming Liu, Jiang Research Institute of Trustworthy Autonomous Systems Dept. of Computer Science and Engineering Southern University of Science and Technology China School of Computer Science University of Birmingham United Kingdom Division of Informatics Imaging and Data Sciences School of Health Sciences University of Manchester United Kingdom MGI Tech Co. Ltd. China Singapore

A comprehensive and explicit understanding of surgical scenes plays a vital role in developing context-aware computer-assisted systems in the operating theatre. However, few works provide systematical analysis to enable hierarchical surgical scene understanding. In this work, we propose to represent the tasks set [phase recognition → step recognition → action and instrument detection] as multi-level semantic scene understanding (MSSU). For this target, we propose a novel hierarchical context transformer (HCT) network and thoroughly explore the relations across the different level tasks. Specifically, a hierarchical relation aggregation module (HRAM) is designed to concurrently relate entries inside multi-level interaction information and then augment task-specific features. To further boost the representation learning of the different tasks, inter-task contrastive learning (ICL) is presented to guide the model to learn task-wise features via absorbing complementary information from other tasks. Furthermore, considering the computational costs of the transformer, we propose HCT+ to integrate the spatial and temporal adapter to access competitive performance on substantially fewer tunable parameters. Extensive experiments on our cataract dataset and a publicly available endoscopic PSI-AVA dataset demonstrate the outstanding performance of our method, consistently exceeding the state-of-the-art methods by a large margin. The code is available at https://***/Aurorahao/HCT. Copyright © 2025, The Authors. All rights reserved.

关键词： Transplantation (surgical)

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Early Prediction of Depressive Episodes in Mood Disorders Using Circadian Rhythm Indicators and Deep Learning

Early Prediction of Depressive Episodes in Mood Disorders Us...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Byeongsu Kim Minsu Chae Yihyun Kim Seokjin Kong Yeongmin Kim Taewon Jung Jaegwon Jeong Soohyun Park Chul-Hyun Cho Ji Won Yeom Taek Lee Heon-Jeong Lee Hwamin Lee Dept. of Biomedical Informatics College of Medicine Korea University Seoul Republic of Korea Dept. of Psychiatry College of Medicine Korea University Seoul Republic of Korea Chronobiology Institute Korea University Seoul Republic of Korea Dept. of Computer Science and Engineering SunMoon University Asan Republic of Korea

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The early prediction of depressive mood episodes is crucial for effective intervention in patients with Major Depressive Disorder (MDD) and Bipolar Disorder (BD). This study explores a predictive framework leveraging digital phenotypic data collected from smartphones and smartwatches, with a focus on circadian rhythm indicators such as Dim Light Melatonin Onset (DLMO). Using data from 164 participants within the Mood Disorder Cohort Research Consortium in Korea, time-series features related to sleep, heart rate, activity levels, and light exposure were processed to predict mood episodes seven days in advance. Deep learning models, including LSTM, GRU, and an LSTM-GRU hybrid, were applied to analyze this data, with the GRU model achieving the highest recall (0.767) and the LSTM model displaying superior robustness across metrics. SHAP value analysis of DLMO-related variables further underscored the association between circadian rhythm disruptions and depressive episodes, with delayed wake-up times relative to ideal schedules linked to increased depressive symptoms. Our findings demonstrate the feasibility of using digital phenotypes for early detection of mood episodes. These results highlight the potential of automated monitoring systems in clinical practice, which enable proactive intervention strategies through continuous, objective monitoring of patient conditions.

关键词： Analytical models Phenotypes Mood Predictive models Data models Robustness Circadian rhythm Long short term memory Smart phones Monitoring

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Directional Metasurface Using Transistor-Loaded Circularly Polarized Antenna Elements

Directional Metasurface Using Transistor-Loaded Circularly P...

引用

Antennas and Propagation Society International Symposium

作者： David Chatzichristodoulou Photos Vryonides Dimitra Psychogiou Symeon Nikolaou Frederick Research Center Nicosia Cyprus RF and Microwave Solutions LTD Cyprus Dept. Electrical Computer Engineering and Informatics Frederick University Nicosia Cyprus School of Engineering University College Cork Cork Ireland Tyndall National Institute Cork Ireland

This paper discusses the RF design of a compact metasurface that enables directional propagation characteristics. It allows for circularly-polarized waves to penetrate through its volume in one direction of propagation whereas it absorbs them in the reverse one. Directionality is achieved by combining two transistors with two circularly-polarized patch antennas in each unit cell of the metasurface. A compact multi-layer integration scheme is proposed and is demonstrated through electromagnetic (EM) simulations at S-band. The proposed metasurface configuration exhibits directional characteristics with the following performance metrics. BW in the forward direction: 3.4-3.7 GHz, maximum Gain in the forward direction: 29 dB, isolation in the reversed direction> 50 dB.

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