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IEEE International Symposium on Biomedical Imaging

作者： Georgia D. Liapi Christos P. Loizou Andrew Nicolaides Maura Griffin Dimitrios Kardoulas Constantinos Pattichis Efthyvoulos Kyriacou Dept. of Electrical Engineering Computer Eng. and Informatics Cyprus University of Technology Limassol Cyprus Vascular Screening and Diagnostic Center Nicosia Cyprus Hemodynamic Unit Vascular Surgery Clinic National and Kapodistrian University of Athens Attikon General University Hospital Athens Greece Dept. of Computer Science University of Cyprus Nicosia Cyprus

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Identification of carotid plaque motion alterations, throughout the cardiac cycle (CC), may reveal rupture-prone plaque components and assist doctors in ischemic stroke risk stratification. In this study, we investigated Ultrasound (U/S) carotid plaque motion, focusing on middle-systole (MS), the interval between early systole (ES) and peak systole (PS) expecting that the dynamic forces applied on the plaque at the beginning of systole are more profound from cardiac ES to MS, compared to PS. We deployed a computer tool, previously developed by researchers of the e-health laboratory at cyprus university of technology, for carotid plaque motion estimation in B-mode U/S videos. We included 16 carotid U/S videos (8 Asymptomatic, AS; 8 Symptomatic, SY patients) and performed video frame (VF) resolution standardization, manual region of interest (ROI) selection, identification of 5 CCs (ES and MS pairs), and plaque motion spread and magnitude measurement, based on dense optical flow. The Maximum Angular Spread (MAXFW 20 , first 20% of the plaque’s overall MAXFW; units: 0° to 360°), a measure we created to determine angle differences between motion vectors, in all plaque areas, can assist in classifying the plaque as concordant (low-strain), moderate(m)-discordant (mid-strain) or discordant (highstrain). We identified 1 concordant, 8 m-discordant, and 7 discordant plaques, with MAXFW 20 ranging from 58±7.45°, 91±43.9°, 156±49.2° (mean±standard deviation), respectively. As our primary findings support our hypothesis, in the future, we will also derive the stress applied on the carotid plaque, in ES and MS.

关键词： Ultrasonic imaging Standardization Stroke (medical condition) Vectors Real-time systems Motion measurement Motion analysis

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Assessing the Electrical Energy Consumption for Designing and Developing an Energy Management System in an Educational Building in Romania 58

Assessing the Electrical Energy Consumption for Designing an...

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58th International Universities Power engineering Conference, UPEC 2023

作者： Farkas, Timea Chicco, Gianfranco Czumbil, Levente Ceclan, Andrei Berciu, Alexandru-George Polycarpou, Alexis Micu, Dan D. Technical University of Cluj-Napoca Energy Transition Research Center Cluj-Napoca Romania Politecnico di Torino Dipartimento Energia 'Galileo Ferraris' Torino Italy Frederick University Department of Electrical Engineering Computer Engineering and Informatics Nicosia Cyprus

ISBN: (纸本)9798350316834

The building sector along with more and more inside or nearby-supplied EV mobility are a key pillar in the efforts of decarbonisation and achieving a Net Zero Emissions scenario by 2050 and even earlier. Without immediate action in the energy management and control automated systems, the building sector will not align with the decarbonisation goals. Energy management systems play a crucial role in managing energy usage and rely on forecasting algorithms and energy management schemes. A case study on a demo site pilot was conducted on the main building of the Faculty of Building engineering Services at the Technical university of Cluj-Napoca, Romania. The study aims to assess electrical energy consumption and bidirectional flows, and to identify key correlations between factors, using multilinear regression as well as consumption pattern identification. Two scenarios are proposed and assessed. © 2023 IEEE.

关键词： Energy efficiency

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AutoNLP: A Framework for Automated Model Selection in Natural Language Processing 18

AutoNLP: A Framework for Automated Model Selection in Natura...

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18th Iberian Conference on Information Systems and Technologies, CISTI 2023

作者： Saleem, Shehan Kumarapathirage, Sapna University of Westminster Computer Science and Engineering London United Kingdom Informatics Institute of Technology School of Computing Colombo06 Sri Lanka

ISBN: (纸本)9789893347928

Although numerous open-source tools exist for machine learning with tabular data, there is a scarcity of comparable resources tailored specifically for NLP. The lack of transparency in the inner workings of existing AutoNLP tools is a significant obstacle in scientific research. AutoML tools are known for their ability to perform model selection with little human intervention, improving the accuracy and reliability of the results. However, conducting a model space search among pre-trained NLP models can be computationally infeasible, making it challenging to determine the optimal NLP model for a given dataset. This research aims to enhance the performance of NLP model selection. Our approach has resulted in higher accuracy than existing methods on the dataset that was created. In our future work, we plan to benchmark our algorithm against datasets created by other researchers to validate its effectiveness. Additionally, we intend to use the same system to perform model selection among popular large language Transformer models. © 2023 ITMA.

关键词： Machine learning

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Modified U-Net with attention gate for enhanced automated brain tumor segmentation

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Neural Computing and Applications 2025年第7期37卷 5521-5558页

作者： Saifullah, Shoffan Dreżewski, Rafal Yudhana, Anton Wielgosz, Maciej Caesarendra, Wahyu Faculty of Computer Science AGH University of Krakow Krakow30-059 Poland Department of Informatics Universitas Pembangunan Nasional Veteran Yogyakarta Yogyakarta55281 Indonesia Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55191 Indonesia Faculty of Computer Science Electronics and Telecommunications AGH University of Krakow Krakow30-059 Poland Faculty of Mechanical Engineering Opole University of Technology 76 Proszkowska St. Opole45-758 Poland

This study addresses the formidable challenges encountered in automated brain tumor segmentation, including the complexities of irregular shapes, ambiguous boundaries, and intensity variations across MRI modalities. Manual segmentation, plagued by subjectivity and time constraints, further exacerbates the problem. To address these issues, we propose a modified U-Net architecture with an integrated attention gate. The proposed model demonstrates high performance, with notable Dice Similarity Coefficient (DSC) and Jaccard Index (JI) values across various tumor classes, consistently exceeding 0.93 and 0.87, respectively. Incorporating Contrast-Limited Adaptive Histogram Equalization and Histogram Equalization improves segmentation accuracy, particularly in cases of Meningioma. Comparative analyses against established models reveal a DSC of 0.9521 and a JI of 0.9093, underscoring the superiority of our method. Validation in the BraTS 2021 dataset underscores the robustness of the method, achieving high DSC and JI scores in four MRI modalities, with the T2 modality demonstrating the highest performance (DSC: 0.9216, JI: 0.8556). While acknowledging these achievements, we recognize challenges related to dataset specificity and computational intensity associated with the attention gate. Future research efforts should address these issues to improve the generalizability and applicability of the method in real-world scenarios. In addition to presenting a novel automated brain tumor segmentation method, this study contributes comprehensive result values and comparative analyses with previous research, providing valuable insights into the evolving landscape of medical image analysis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Image segmentation

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Directional Metasurface Using Transistor-Loaded Circularly Polarized Antenna Elements

Directional Metasurface Using Transistor-Loaded Circularly P...

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2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023

作者： Chatzichristodoulou, David Vryonides, Photos Psychogiou, Dimitra Nikolaou, Symeon Frederick Research Center Nicosia Cyprus Rf and Microwave Solutions Ltd Cyprus School of Engineering University College Cork CorkT12 K8AF Ireland Tyndall National Institute CorkT12 R5CP Ireland Frederick University Dept. Electrical Computer Engineering and Informatics Nicosia Cyprus

ISBN: (纸本)9781665442282

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. © 2023 IEEE.

关键词： Microstrip antennas

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MMDDI-MGPFF: Multi-Modal Drug Representation Learning with Molecular Graph and Pharmacological Feature Fusion for Drug-Drug Interaction Event Prediction

MMDDI-MGPFF: Multi-Modal Drug Representation Learning with M...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Guishen Guo, Zhitong You, Guilin Xu, Ming Cao, Chen Hu, Xiaowen School of Life Sciences Jilin University Changchun China College of Computer Science and Engineering Changchun University of Technology Changchun China Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China

ISBN: (纸本)9798350386226

Drug-drug interactions pose a significant challenge in healthcare, directly impacting patient safety and treatment efficacy. Although recent advances in computational methods have improved drug-drug interaction (DDI) event prediction, many existing models face difficulties in effectively fusing features across different DDI tasks. To address these limitations, we introduce MMDDI-MGPFF, a novel multi-modal drug representation learning framework that integrates molecular graphs and pharmacological feature fusion to improve DDI prediction. Our model leverages a graph isomorphism network (GIN) for efficient encoding of molecular structures, coupled with an autoencoder for learning sequence-based drug features, encompassing both biological and pharmacological characteristics. We propose a multi-modal fusion approach that employs multi-head attention and deep neural networks to seamlessly integrate these graph and sequence modalities. Comprehensive experiments on a benchmark dataset demonstrate that MMDDI-MGPFF significantly outperforms state-of-the-art methods in DDI prediction tasks. Ablation studies further corroborate the efficacy of our model's components, particularly the GIN and the integration of multi-feature drug representations. This work advances the field of DDI prediction by providing a more holistic and accurate approach to drug representation and interaction modeling. © 2024 IEEE.

关键词： Deep neural networks

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RGB-guided hyperspectral image super-resolution with deep progressive learning

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CAAI Transactions on Intelligence technology 2024年第3期9卷 679-694页

作者： Tao Zhang Ying Fu Liwei Huang Siyuan Li Shaodi You Chenggang Yan School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Satellite Information Intelligent Processing and Application Research Laboratory Beijing Institute of Remote SensingBeijingChina Key Laboratory of Spectral Imaging Technology Xi'an Institute of Optics and Precision Mechanics of Chinese Academy of SciencesXi'anChina Institute of Informatics University of AmsterdamAmsterdamNetherlands School of Communication Engineering Hangzhou Dianzi UniversityHangzhouChina

Due to hardware limitations,existing hyperspectral(HS)camera often suffer from low spatial/temporal ***,it has been prevalent to super-resolve a low reso-lution(LR)HS image into a high resolution(HR)HS image with a HR RGB(or mul-tispectral)image *** approaches for this guided super-resolution task often model the intrinsic characteristic of the desired HR HS image using hand-crafted ***,researchers pay more attention to deep learning methods with direct supervised or unsupervised learning,which exploit deep prior only from training dataset or testing *** this article,an efficient convolutional neural network-based method is presented to progressively super-resolve HS image with RGB image ***-ically,a progressive HS image super-resolution network is proposed,which progressively super-resolve the LR HS image with pixel shuffled HR RGB image ***,the super-resolution network is progressively trained with supervised pre-training and un-supervised adaption,where supervised pre-training learns the general prior on training data and unsupervised adaptation generalises the general prior to specific prior for variant testing *** proposed method can effectively exploit prior from training dataset and testing HS and RGB images with spectral-spatial *** has a good general-isation capability,especially for blind HS image *** experimental results show that the proposed deep progressive learning method out-performs the existing state-of-the-art methods for HS image super-resolution in non-blind and blind cases.

关键词： computer vision deep neural networks image processing image resolution unsupervised learning

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Context-aware Container Orchestration in Serverless Edge Computing

Context-aware Container Orchestration in Serverless Edge Com...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Guan, Peiyuan Chen, Chen Chen, Ziru Cai, Lin X. Hao, Xing Taherkordi, Amir University of Oslo Dept. of Informatics Oslo Norway University of Cambridge Dept. of Computer Science and Technology Cambridge United Kingdom Illinois Institute of Technology Dept. of Electrical and Computer Engineering Chicago United States Northwest Univesity School of Information Science and Technology Xi'an China

ISBN: (纸本)9798350351255

Adopting serverless computing to edge networks benefits end-users from the pay-as-you-use billing model and flexible scaling of applications. This paradigm extends the boundaries of edge computing and remarkably improves the quality of services. However, due to the heterogeneous nature of computing and bandwidth resources in edge networks, it is challenging to dynamically allocate different resources while adapting to the burstiness and high concurrency in serverless workloads. This article focuses on serverless function provisioning in edge networks to optimize end-to-end latency, where the challenge lies in jointly allocating wireless bandwidth and computing resources among heterogeneous computing nodes. To address this challenge, We devised a context-aware learning framework that adaptively orchestrates a wide spectrum of resources and jointly considers them to avoid resource fragmentation. Extensive simulation results justified that the proposed algorithm reduces over 95% of converge time while the end-to-end delay is comparable to the state of the art. © 2024 IEEE.

关键词： Quality of service

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Urban Pluvial Flood Prediction Using Machine Learning Models 4

Urban Pluvial Flood Prediction Using Machine Learning Models

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4th International Conference on Innovations in computer Science, ICONICS 2024

作者： Waseemullah Hassan, Tahir Majeed, Sajid Memon, Muhammad Qasim NED University of Engineering and Technology Department of Computer Science & Information Technology Karachi Pakistan Institute of Space Technology Department of Remote Sensing & Geo-Informatics Karachi Pakistan University of Sufism and Modern Sciences Department of Information and Computing Bhitshah Pakistan

ISBN: (纸本)9798331504342

Urban pluvial flooding causes threats to human lives, economy, infrastructure, and ecosystems around the world, particularly in developing countries. Pakistan is among those developing countries that have become vulnerable to urban flooding due to global warming, climate change risks, rapid urbanization, and the occurrence of extreme and unusual rainfall events in the monsoon season. To effectively mitigate the adverse effects of pluvial flooding, there is a pressing need for an advanced and accurate predictive model that is capable of predicting urban pluvial flooding. The aim of this research is to utilize the potential of machine learning (ML) techniques and algorithms to accurately predict the occurrence of urban pluvial flooding, for this study we have worked on the monsoon rainfall data of multiple cities of Sindh province in Pakistan. We made an attempt to use multiple ML models especially logistic regression (LR), random forest (RF), k nearest neighbor (KNN), and support vector machine (SVM) to propose the best and accurate predictive model. The developed models were evaluated and assessed using various performance evaluation metrics which includes accuracy, recall, precision and F1 score. The best among proposed models was created using LR which achieved an accuracy of 97%, while the accuracy of other models which were created using SVM, KNN and RF were 96%, 93%, and 92% respectively. © 2024 IEEE.

关键词： Prediction models

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Deep Learning Approaches for Stress Detection: A Survey

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IEEE Transactions on Affective Computing 2024年第2期16卷 499-517页

作者： Kyrou, Maria Kompatsiaris, IoannisSenior Petrantonakis, Panagiotis C. Aristotle University of Thessaloniki Artificial Intelligence and Information Analysis Laboratory Dept. of Informatics GR 54124 Greece Information Technologies Institute Centre for Research and Technology Hellas GR 57001 Greece Aristotle University of Thessaloniki Dept. of Electrical and Computer Engineering GR 54124 Greece

Stress has a severe impact on individuals irrespective of age, sex, work, or background. The reliable development of stress detection techniques enhances the social, educational, physical, economic, and professional quality of life, preventing chronic stress and proposing alleviation strategies. Research studies examine psychological, cognitive, behavioral, and physiological reactions to identify stress adequately. Deep Learning (DL) has received significant attention in recent years as it deals with high-dimensional, heterogeneous data and automatically learns representative features. This paper presents a survey on stress detection with recent DL approaches, leveraging data from all possible sources (physiological, speech, facial expressions, gestures, and social media content). The methodological outlines, the best results, and the main contributions of each study are discussed. We also describe publicly available datasets used by several of the presented works. Finally, we emphasize various open issues within the field of research and highlight key directions for future work © 2024 IEEE.

关键词： Social psychology

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