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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Kamil Canberk Atik Enes Erdogan Ahmet Yusuf Yahsi Feyzullah Kara Baris Yalcin Gü rkan Cetin Nazim Kemal Ure Faculty of Aeronautics and Astronautics ITU Artificial Intelligence and Data Science Application and Research Center Istanbul Technical University Istanbul Turkey Faculty of Computer and Informatics Engineering ITU Artificial Intelligence and Data Science Application and Research Center Istanbul Technical University Istanbul Turkey Faculty of Electrical and Electronics ITU Artificial Intelligence and Data Science Application and Research Center Istanbul Technical University Istanbul Turkey Havelsan Ankara Turkey

This paper presents a methodology for enhancing the localization performance of drone swarms by collaborative propagation of the estimates of drones equipped with higher cost sensors to the drones with lower-cost sensors. Although recent advances in visual-inertial odometry (VIO) enabled good performance in GPS denied environments, the localization performance is still highly dependent on the accuracy of inertial sensors. Moreover, environmental effects that corrupt visual inputs also contribute to the error rate of VIO estimates thus localization in noisy environments with low-grade sensors is still widely an open problem. We show that this problem can be alleviated when drones are navigating in the form of a swarm, and the relative distance measurements between drones are available through ultra-wideband (UWB) signals. In particular, we develop a collaborative Kalman filtering and optimization framework, where the drones with lower grade sensors receive updates from drones that have access to higher-quality measurements. We validate our approach in a simulation environment with realistic visual representations and show that the proposed methodology can significantly improve the localization performance, especially for the scenarios where the camera measurements are corrupted with high noise. We further present results in simplistic flight tests to demonstrate the applicability of our approach to real hardware.

关键词： Location awareness Visualization Costs Navigation Surveillance Teamwork Trajectory

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Chaotic American zebra search optimization algorithm for benchmark challenges

Chaotic American zebra search optimization algorithm for ben...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Vikram Kumar Kamboj Ardhala Bala Krishna Chaman Verma Maria Simona Raboaca Constantin Filote Maria Stepanova Schulich School of Engineering University of Calgary Alberta Canada Dept. of Electrcical and Electronics Engineering Malla Reddy Engineering College for Women Hyderabad India Dept. of Media and Edu. Informatics Faculty of Informatics Eötvös Loránd University Budapest Hungary ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Romania Faculty of Electrical Engineering and Computer Science Stefan cel Mare University of Suceava Suceava Romania Engineering Business and Management Faculty Bauman Moscow State Technical University Russia

In this research, we presented a modification to the recently developed swarm premised American zebra search optimization algorithm (AZOA) using chaos. The particular Singer mapping was proposed by the so-called chaotic AZOA and was already known to perform better in optimization. Twelve uni, multi modal benchmark functions, three-bar truss and ten bar truss optimal structural designs were tested. The chaotic AZOA with Singer map would perform more effectively, more consistently, and quicker than the classical AZOA and other recent metaheuristics in optimization, according to the results, which also supported the viability of the modifications. It was discussed if the chaotic AZAO could be optimized with the original AZOA, and the chaotic AZOA was suggested for use in applications for actual engineering challenges.

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Transformers in medical image analysis

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Intelligent Medicine 2023年第1期3卷 59-78页

作者： Kelei He Chen Gan Zhuoyuan Li Islem Rekik Zihao Yin Wen Ji Yang Gao Qian Wang Junfeng Zhang Dinggang Shen Medical School of Nanjing University NanjingJiangsu 210093China National Institute of Healthcare Data Science at Nanjing University NanjingJiangsu 210093China BASIRA Laboratory Faculty of Computer and Informatics EngineeringIstanbul Technical UniversityIstanbulTurkey School of Science and Engineering ComputingUniversity of DundeeUK State Key Laboratory for Novel Software Technology Nanjing UniversityNanjingJiangsu 210093China School of Biomedical Engineering ShanghaiTech UniversityShanghai 201210China Department of Research and Development Shanghai United Imaging Intelligence Co.Ltd.Shanghai 200030China Shanghai Clinical Research and Trial Center Shanghai 201703China

Transformers have dominated the field of natural language processing and have recently made an impact in the area of computer *** the field of medical image analysis,transformers have also been successfully used in to full-stack clinical applications,including image synthesis/reconstruction,registration,segmentation,detection,and *** paper aimed to promote awareness of the applications of transformers in medical image ***,we first provided an overview of the core concepts of the attention mechanism built into transformers and other basic ***,we reviewed various transformer architectures tailored for medical image applications and discuss their *** this review,we investigated key challenges including the use of transformers in different learning paradigms,improving model efficiency,and coupling with other *** hope this review would provide a comprehensive picture of transformers to readers with an interest in medical image analysis.

关键词： Transformer Medical image analysis Deep learning Diagnosis Registration Segmentation Image synthesis Multi-task learning Multi-modal learning Weakly-supervised learning

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Predicting cognitive scores with graph neural networks through sample selection learning

arXiv

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arXiv 2021年

作者： Hanik, Martin Demirtaş, Mehmet Arif Gharsallaoui, Mohammed Amine Rekik, Islem Zuse Institute Berlin Berlin Germany BASIRA Lab Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey Tunis Tunisia BASIRA Lab Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey Computing School of Science and Engineering University of Dundee Dundee United Kingdom

Analyzing the relation between intelligence and neural activity is of the utmost importance in understanding the working principles of the human brain in health and disease. In existing literature, functional brain connectomes have been used successfully to predict cognitive measures such as intelligence quotient (IQ) scores in both healthy and disordered cohorts using machine learning models. However, existing methods resort to flattening the brain connectome (i.e., graph) through vectorization which overlooks its topological properties. To address this limitation and inspired from the emerging graph neural networks (GNNs), we design a novel regression GNN model (namely RegGNN) for predicting IQ scores from brain connectivity. On top of that, we introduce a novel, fully modular sample selection method to select the best samples to learn from for our target prediction task. However, since such deep learning architectures are computationally expensive to train, we further propose a learning-based sample selection method that learns how to choose the training samples with the highest expected predictive power on unseen samples. For this, we capitalize on the fact that connectomes (i.e., their adjacency matrices) lie in the symmetric positive definite (SPD) matrix cone. Our results on full-scale and verbal IQ prediction outperforms comparison methods in autism spectrum disorder cohorts and achieves a competitive performance for neurotypical subjects using 3-fold cross-validation. Furthermore, we show that our sample selection approach generalizes to other learning-based methods, which shows its usefulness beyond our GNN architecture. © 2021, CC BY.

关键词： Forecasting

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MGN-Net: A multi-view graph normalizer for integrating heterogeneous biological network populations

arXiv

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arXiv 2021年

作者： Gürbüz, Mustafa Burak Rekik, Islem BASIRA lab Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey School of Science and Engineering Computing University of Dundee United Kingdom

With the recent technological advances, biological datasets, often represented by networks (i.e., graphs) of interacting entities, proliferate with unprecedented complexity and heterogeneity. Although modern network science opens new frontiers of analyzing connectivity patterns in such datasets, we still lack data-driven methods for extracting an integral connectional fingerprint of a multi-view graph population, let alone disentangling the typical from the atypical variations across the population samples. We present the multi-view graph normalizer network (MGN-Net1), a graph neural network based method to normalize and integrate a set of multi-view biological networks into a single connectional template that is centered, representative, and topologically sound. We demonstrate the use of MGN-Net by discovering the connectional fingerprints of healthy and neurologically disordered brain network populations including Alzheimer's disease and Autism spectrum disorder patients. Additionally, by comparing the learned templates of healthy and disordered populations, we show that MGN-Net significantly outperforms conventional network integration methods across extensive experiments in terms of producing the most centered templates, recapitulating unique traits of populations, and preserving the complex topology of biological networks. Our evaluations showed that MGN-Net is powerfully generic and easily adaptable in design to different graph-based problems such as identification of relevant connections, normalization and integration. © 2021, CC BY-NC-SA.

关键词： Graphic methods

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Brain graph super-resolution using adversarial graph neural network with application to functional brain connectivity

arXiv

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arXiv 2021年

作者： Isallari, Megi Rekik, Islem BASIRA Lab Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey School of Science and Engineering Computing University of Dundee United Kingdom

Brain image analysis has advanced substantially in recent years with the proliferation of neuroimaging datasets acquired at different resolutions. While research on brain image super-resolution has undergone a rapid development in the recent years, brain graph super-resolution is still poorly investigated because of the complex nature of non-Euclidean graph data. In this paper, we propose the first-ever deep graph super-resolution (GSR) framework that attempts to automatically generate high-resolution (HR) brain graphs with N0 nodes (i.e., anatomical regions of interest (ROIs)) from low-resolution (LR) graphs with N nodes where N 0. First, we formalize our GSR problem as a node feature embedding learning task. Once the HR nodes' embeddings are learned, the pairwise connectivity strength between brain ROIs can be derived through an aggregation rule based on a novel Graph U-Net architecture. While typically the Graph U-Net is a node-focused architecture where graph embedding depends mainly on node attributes, we propose a graph-focused architecture where the node feature embedding is based on the graph topology. Second, inspired by graph spectral theory, we break the symmetry of the U-Net architecture by super-resolving the low-resolution brain graph structure and node content with a GSR layer and two graph convolutional network layers to further learn the node embeddings in the HR graph. Third, to handle the domain shift between the ground-truth and the predicted HR brain graphs, we incorporate adversarial regularization to align their respective distributions. Our proposed AGSR-Net framework outperformed its variants for predicting high-resolution functional brain graphs from low-resolution ones. Our AGSR-Net code is available on GitHub at https://***/basiralab/AGSR-Net. © 2021, CC BY-NC-SA.

关键词： Graphic methods

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Tuning Accuracy-Diversity Trade-off in Neural Network Ensemble via Novel Entropy Loss Function

Tuning Accuracy-Diversity Trade-off in Neural Network Ensemb...

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International Conference on Electrical and Electronics engineering, ELECO

作者： Muhammad Ammar Ali Yusuf Sahin Süreyya Özöğür-Akyüz Gozde Unal Buse Cisil Otar Graduate School of Natural and Applied Sciences Bahcesehir University Istanbul Turkey Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Turkey

ISBN: (纸本)9781665409407

Ensemble methods are used in machine learning by combining several models which produce an optimal predictive model. Neural network ensemble learning is a technique, which uses multiple individual deep neural networks (DNNs). Ensemble pruning methods are used to reduce the computational complexity of ensemble models. In this study, a novel optimization model is proposed to increase error independence in classifiers via entropy measurement and thus better prune the ensemble. An ensemble of 300 DNNs is trained and tested on the CIFAR-10 dataset and results show an increase in accuracy while main-taining a level of relative entropy measured by Kullback-Leibler divergence (KL-divergence).

关键词： Deep learning Computational modeling Neural networks Measurement uncertainty Diversity reception Predictive models Entropy

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Quantifying the reproducibility of graph neural networks using multigraph brain data

arXiv

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arXiv 2021年

作者： Gharsallaoui, Mohammed Amine Rekik, Islem BASIRA Lab. Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey School of Science and Engineering Computing University of Dundee United Kingdom

Graph neural networks (GNNs) have witnessed an unprecedented proliferation in tackling several problems in computer vision, computer-aided diagnosis and related fields. While prior studies have focused on boosting the model accuracy, quantifying the reproducibility of the most discriminative features identified by GNNs is still an intact problem that yields concerns about their reliability in clinical applications in particular. Specifically, the reproducibility of biological markers across clinical datasets and distribution shifts across classes (e.g., healthy and disordered brains) is of paramount importance in revealing the underpinning mechanisms of diseases as well as propelling the development of personalized treatment. Motivated by these issues, we propose, for the first time, reproducibility-based GNN selection (RG-Select), a framework for GNN reproducibility assessment via the quantification of the most discriminative features (i.e., biomarkers) shared between different models. To ascertain the soundness of our framework, the reproducibility assessment embraces variations of different factors such as training strategies and data perturbations. Despite these challenges, our framework successfully yielded replicable conclusions across different training strategies and various clinical datasets. Our findings could thus pave the way for the development of biomarker trustworthiness and reliability assessment methods for computer-aided diagnosis and prognosis tasks. RG-Select code is available on GitHub at https://***/basiralab/RG-Select. © 2021, CC BY-NC-ND.

关键词： Biomarkers

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Efficient Resource Management in IoTs Using Evolutionary and Swarm Intelligence Algorithms

Efficient Resource Management in IoTs Using Evolutionary and...

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Innovative Approaches in Smart Technologies (ISAS), International Symposium on

作者： Keshav Dev Gupta Korhan Cengiz Charu Awasthi Siva Akash Ramini Shyamapriya Chatterjee M Pavithra Department of Computer Science Engineering Poornima College of Engineering jaipur India College of Information Technology University of Fujairah UAE Department of Computer Engineering Istinye University Istanbul Turkey Department of Information Technologies Faculty of Informatics and Management University of Hradec Kralove Kralove Czech Republic Department of information technology JSS academy of Technical Education Noida India Department of School of Computer Science and Engineering (SCOPE) VIT-AP University Amaravati Andhra Pradesh Department of Information Technology Narula Institute of Technology Kolkata West Bengal Department of Computer Science and Engineering Jansons Institute of Technology Karumathampatti Coimbatore

The proliferation of the Internet of Things (IoT) has ushered in a transformative era of connected devices, emphasizing the critical need for effective resource management. This study introduces an innovative approach that harnesses Evolutionary and Swarm Intelligence algorithms for IoT Task Scheduling, addressing resource optimization challenges. The approach offers superior resource utilization through advanced optimization techniques, reducing energy consumption and enhancing efficiency. Furthermore, it significantly reduces task scheduling execution time, enabling prompt decision-making in dynamic IoT environments. This results in improved task allocation quality, meeting application-specific requirements, and prioritizing critical tasks. Notably, the approach excels in minimizing completion time, making it particularly advantageous for real-time IoT applications. This research contributes to the advancement of IoT resource management, offering an adaptive, efficient, and intelligent solution with broad applicability. In the evolving landscape of IoT, the findings presented here lay a solid foundation for future research and practical implementations, ultimately promoting more responsive, sustainable, and intelligent IoT ecosystems.

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Dual Band Planar Antenna For Millimeter Wave Applications

Dual Band Planar Antenna For Millimeter Wave Applications

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Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： Sarra Khacha Djamel Sayad Mohamed Lamine Bouknia Maroua Nehaoua Yamina Tighilt Kamil Karaçuha Jonathan Rodriguez Issa Elfergani Chemseddine Zebiri Laboratoire d'Electronique de puissance et commande industrielle University of Ferhat Abbas Setif -1 Algeria Dept. of Electrical Engineering Laboratoire d'Electrotechnique de Skikda (LES) University of 20 Aout 1955 Skikda Algeria Electrical Engineering Department Istanbul Technical University Istanbul Turkey Instituto de Telecomunicações Campus Universitário de Santiago Aveiro Portugal Faculty of Engineering and Informatics University of Bradford Bradford UK

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

This article presents a dual-band planar antenna printed on an CaO-B2O3-SiO2 glass-ceramics substrate for millimeter-wave applications. The designed antenna has an overall size of 9 mm× 9 mm. The antenna exhibits good performance in terms of bandwidth, gain, and radiation efficiency, making it a good candidate for millimeter-wave (mmWave).

关键词： Conferences Dual band Planar arrays Bandwidth Millimeter wave communication Antennas Gain

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