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IEEE International Symposium on Biomedical Imaging

作者： Amelie Rauland Dorit Merhof Institute of Imaging and Computer Vision RWTH Aachen University Aachen German Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Several studies have investigated the possibility of predicting the fiber orientation distribution function (fODF), which is obtained using the very accurate multi-shell multi-tissue constrained spherical deconvolution (MT-CSD) from single-shell or low angular resolution multi-shell diffusion magnetic resonance imaging (dMRI) data using deep *** all these approaches show promising results, the vast majority have in common that they require multi-shell high angular resolution diffusion imaging (HARDI) data to calculate the ground truth fODF using the MT-CSD for training their networks. This data, however, is difficult to acquire in a clinical context and it is yet unclear how well networks trained on data acquired on a certain scanner with a certain protocol would generalize to different *** this work, we address these shortcomings and present a method that can estimate an accurate fODF from single-shell diffusion data without the need for multi-shell data for training. This is achieved by generating patient-, acquisition-and scanner-specific synthetic single voxel diffusion signals with a known ground truth fODF from single shell data that can be used to train the neural network. The trained network will then be applied to the real patient data to predict the fODF with a quality standard close to that of an MT-CSD and the ability to determine if white matter (WM) is present in the underlying *** approach is evaluated on 20 subjects from the Human Connectome Project (HCP) for all three shells (b=1000, 2000, 3000 s/mm 2 ). When comparing both this approach and a single shell constrained spherical deconvolution (CSD) to the results of the MT-CSD, this work outperforms the single shell CSD in terms of the angular correlation coefficient and root mean squared error on all three shells.

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Criteria and Tips for Choosing Suitable Journal for Your Paper

arXiv

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arXiv 2023年

作者： Hammad, Mohamed EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia Information Technology Department Faculty of Computers and Information Menoufia University Egypt

Selecting the right journal for your research paper is a pivotal decision in the academic publishing journey. This paper aims to guide researchers through the process of choosing a suitable journal for their work by discussing key criteria and offering practical tips. The significance of this decision lies in the potential impact of your research, the alignment of author’s findings with the journal's scope, and the reception your work receives in the scientific community. In this paper, we delve into the importance of meticulously matching your paper's subject matter with the journal's scope, assessing the journal's reputation and impact factor, understanding the intricacies of the submission process, and upholding ethical and open-access considerations. By following these guidelines, researchers can make informed decisions that increase the chances of their work being published in a respected journal that truly complements their research objectives and *** Codes K.6 © 2023, CC BY.

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Smart Surveillance for COVID-19 Health Protocol Compliance in Vehicles 21

Smart Surveillance for COVID-19 Health Protocol Compliance i...

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Proceedings of the 2021 International Conference on computer, Control, Informatics and Its Applications

作者： Rachmat Hidayat Aniati Murni Arymurthy Heru Susanto Faculty of Computer Science University of Indonesia Indonesia and Data and Information Center National Research and Innovation Agency Indonesia Faculty of Computer Science University of Indonesia Indonesia Research Center for Informatics National Research and Innovation Agency Indonesia and Tunghai University Taiwan

ISBN: (纸本)9781450385244

Nowadays, the Corona Virus outbreak in 2019 (COVID-19) has become a global pandemic. The public must implement health protocols to reduce the spread of COVID-19. Trends show that the number of COVID-19 is increasing over time. This study proposes and develops a smart model to detect COVID-19 Health protocol violators in vehicles. This model can detect violations of the use of masks and social distancing in vehicles. The proposed model is a combination of the YOLO object detection method and the Hourglass architecture. The experimental results of the proposed model can detect violations with a high success rate. Here, the standard YOLOv4 detection model as baseline yields an mAP of 0.87 for validation and 0.74 for test data. On the other hand, the proposed method produces an mAP of 0.92 on the validation data, 0.78 on the test data. From these results, this smart model is quite promising to help reduce the spread of COVID-19.

关键词： YOLO Deep Learning Vehicles Object Detection COVID-19 Health Protocol Hourglass

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Early Detection of Breast Cancer with Microcalcifications on Mammography Using Deep Learning

Early Detection of Breast Cancer with Microcalcifications on...

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Ashraf Osman Ibrahim Hafia Mamoun Ismail Abuharaz Mohammed A Saleh Razan Alharith Department of Computer and Information Sciences Universiti Teknologi PETRONAS Seri Iskandar Perak Malaysia Depeartment of Computer Science Al Neelain University Khartoum Sudan DaTA Research Group Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Malaysia School of Computing and Artificial Intelligence Southwest Jiaotong University

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Breast cancer is among the leading causes of death in women globally, making early detection critical for improving survival rates. Microcalcifications (MCs) on mammographic images are important markers for early detection of breast cancer; yet, identifying and interpreting them can be difficult. Conventional diagnostic procedures sometimes face obstacles due to the complexity and nuance of MC patterns, resulting in higher percentages of missed diagnoses and false positives. This paper develops a deep convolutional neural network (CNN) model to increase the detection and classification accuracy of microcalcifications (MCs) in mammographic images. dataset of 1,093 mammography images used, the proposed model reaches a remarkable training accuracy of 99.98% and testing accuracy of 90.37%. The model's excellent accuracy and low overfitting highlight its potential to assist radiologists in the early detect and diagnose of breast cancer, thereby improving patient outcomes. This study's contribution is the innovative use of advanced deep learning algorithms to a major issue in medical imaging, which represents a significant improvement over current diagnostic approaches.

关键词： Deep learning Training Accuracy Biological system modeling Breast cancer Mammography data models Convolutional neural networks Testing Overfitting

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Federated Learning Across Decentralized and Unshared Archives for Remote Sensing Image Classification

arXiv

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arXiv 2023年

作者： Büyüktaş, Barış Sumbul, Gencer Demir, Begüm The Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Berlin10623 Germany The BIFOLD - Berlin Institute for the Foundations of Learning and Data Berlin10623 Germany Sion1950 Switzerland

Federated learning (FL) enables the collaboration of multiple deep learning models to learn from decentralized data archives (i.e., clients) without accessing data on clients. Although FL offers ample opportunities in knowledge discovery from distributed image archives, it is seldom considered in remote sensing (RS). In this paper, as a first time in RS, we present a comparative study of state-of-the-art FL algorithms for RS image classification problems. To this end, we initially provide a systematic review of the FL algorithms presented in the computer vision and machine learning communities. Then, we select several state-of-the-art FL algorithms based on their effectiveness with respect to training data heterogeneity across clients (known as non-IID data). After presenting an extensive overview of the selected algorithms, a theoretical comparison of the algorithms is conducted based on their: 1) local training complexity;2) aggregation complexity;3) learning efficiency;4) communication cost;and 5) scalability in terms of number of clients. After the theoretical comparison, experimental analyses are presented to compare them under different decentralization scenarios. For the experimental analyses, we focus our attention on multi-label image classification problems in RS. Based on our comprehensive analyses, we finally derive a guideline for selecting suitable FL algorithms in RS. The code of this work is publicly available at https://***/rsim/FL-RS. © 2023, CC BY.

关键词： Image classification

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Multi-Layer Transmitting RIS-Aided Receiver for Collaborative Jamming and Anti-Jamming Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Li, Junjie Yang, Liang Hao, Wanming Ahmad, Ishtiaq Liu, Hongwu Shu, Feng Niyato, Dusit Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Hunan University College of Computer Science and Electronic Engineering China Jishou University School of Communication and Electronic Engineering China Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China Czech Technical University in Prague Faculty of Electrical Engineering Department of Telecommunication Engineering Prague166 27 Czech Republic Shandong Jiaotong University School of Information Science and Electrical Engineering Jinan250357 China Hainan University School of Information and Communication Engineering Haikou570228 China Nanyang Technological University College of Computing and Data Science Singapore

In this paper, we propose a novel architecture for a multi-layer active-passive cascade transmitting reconfigurable intelligent surface (RIS)-aided receiver. We aim at enhancing the scalability of antenna dimensions and energy efficiency for user equipment (UE), as well as improving the amplitude freedom of antenna gain. This architecture integrates reflecting RIS for applications in both jamming and anti-jamming scenarios. We formulate a problem to maximize the worst-case spectral efficiency (SE) of the UE, based on imperfect channel state information (CSI) of the malicious device, thereby ensuring the SE of our UE while disrupting the signal reception of the illegal UE. To address the inherently non-convex nature of the formulated problem, we propose an alternating optimization framework, which decomposes the main problem into several subproblems. Specifically, we uniformly discretize the uncertain domains to obtain robust CSI, allowing us to determine an optimal receiver vector. To balance computational complexity and performance, we propose solutions for the subproblems of base station beamforming and coefficients with different patterns of RIS. Furthermore, to effectively disrupt malicious inter-device communication while avoiding detection and localization, we develop a collaborative interference pattern incorporating silent interference and non-interference protocols. Importantly, the pattern carefully balances performance with the overhead of CSI acquisition. Finally, simulation results validate the efficiency of the proposed algorithms, demonstrating that the proposed architecture achieves better jamming and anti-jamming performance. © 2002-2012 IEEE.

关键词： Jamming

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A New Large Language Model for Attribute Extraction in E-Commerce Product Categorization

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Electronics (Switzerland) 2025年第10期14卷 1930-1930页

作者： Çiftlikçi, Mehmet Serhan Çakmak, Yusuf Kalaycı, Tolga Ahmet Abut, Fatih Akay, Mehmet Fatih Kızıldağ, Mehmet Department of Data Science Istanbul Trendyol34485 Turkey Department of Computer Engineering Faculty of Engineering Çukurova University Adana01250 Turkey BADEM Bilgi Sistemleri Danışmanlık Sağlık Hizm. Tic. Ltd. Şti Adana01250 Turkey

In the rapidly evolving field of e-commerce, precise and efficient attribute extraction from product descriptions is crucial for enhancing search functionality, improving customer experience, and streamlining the listing process for sellers. This study proposes a large language model (LLM)-based approach for automated attribute extraction on Trendyol’s e-commerce platform. For comparison purposes, a deep learning (DL) model is also developed, leveraging a transformer-based architecture to efficiently identify explicit attributes. In contrast, the LLM, built on the Mistral architecture, demonstrates superior contextual understanding, enabling the extraction of both explicit and implicit attributes from unstructured text. The models are evaluated on an extensive dataset derived from Trendyol’s Turkish-language product catalog, using performance metrics such as precision, recall, and F1-score. Results indicate that the proposed LLM outperforms the DL model across most metrics, demonstrating superiority not only in direct single-model comparisons but also in average performance across all evaluated categories. This advantage is particularly evident in handling complex linguistic structures and diverse product descriptions. The system has been integrated into Trendyol’s platform with a scalable backend infrastructure, employing Kubernetes and Nvidia Triton Inference Server for efficient bulk processing and real-time attribute suggestions during the product listing process. This study not only advances attribute extraction for Turkish-language e-commerce but also provides a scalable and efficient NLP-based solution applicable to large-scale marketplaces. The findings offer critical insights into the trade-offs between accuracy and computational efficiency in large-scale multilingual NLP applications, contributing to the broader field of automated product classification and information retrieval in e-commerce ecosystems. © 2025 by the authors.

关键词： Marketplaces

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Visible Light Positioning Method for Indoor Line-of-Sight Scene Based on Time Series Coding

Visible Light Positioning Method for Indoor Line-of-Sight Sc...

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IEEE International Conference on data science in Cyberspace (DSC)

作者： Yonghao Yu Yongde Guo Dawei Zhao Kexue Fu Yongwei Tang Qinyuan Wang Faculty of Data Science City University of Macau China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center(National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China School of Mechanical Engineering Key Laboratory of High Efficiency and Clean Mechanical Manufacture Ministry of Education Shandong University Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Existing indoor visible light positioning methods, such as those based on fingerprint database algorithms, are complex to construct and consume significant resources. In response to this issue, the text proposes an end-to-end indoor visible light positioning method that samples continuous signals at a small number of random coordinate positions in indoor environments. Through a deep learning method based on attention mechanism, a nonlinear mapping between the received signal and spatial position coordinates is constructed, and the time series properties in the received signal are encoded as a feature in the data. The method is further improved by combining residual networks to improve accuracy. Through simulation experiments, it has been verified that the deep learning based localization method described in this article can achieve sub meter level accuracy on small sample datasets, and it has been proven that time series features have a significant impact on localization accuracy in indoor visible light positioning algorithms based on deep learning.

关键词： Location awareness Deep learning Meters Training Accuracy Attention mechanisms Time series analysis Training data Encoding data models

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Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning

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Procedia computer science 2023年 216卷 57-66页

作者： Mira Hayati Kahlil Muchtar Roslidar Novi Maulina Irfan Syamsuddin Gregorius Natanael Elwirehardja Bens Pardamean Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh 23111 Indonesia Telematics Research Center (TRC) Universitas Syiah Kuala Banda Aceh 23111 Indonesia Faculty of Medicine Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Computer and Networking Engineering Politeknik Negeri Ujung Pandang Makassar 90245 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Diabetic retinopathy (DR) is a type of diabetes mellitus that attacks the retina of the eye. DR will cause patients to experience blindness slowly. Generally, DR can be detected by using a special instrument called an ophthalmoscope to view the inside of the eyeball. However, in conditions where there is a very small difference between the normal image and the DR image, computer-based assistance is needed for maximizing image reading value. In this research, a method of image quality improvement will be carried out which will then be integrated with a classification algorithm based on deep learning. The results of image improvement using Contrast Limited Adaptive Histogram Equalization (CLAHE) shows that the average accuracy of the method on several models is very competitive, 91% for the VGG16 model, 95% for InceptionV3, and 97% for EfficientNet compared to the results original image which only has an accuracy of 87% for VGG16 model, 90% for InceptionV3 model, and 95% for EfficientNet. However, in ResNet34 better accuracy is obtained in the original image with an accuracy of 95% while in the CLAHE image the accuracy value is only 84%. The results of this comprehensive evaluation and recommendation of famous backbone networks can be useful in the computer-aided diagnosis of diabetic retinopathy.

关键词： Diabetic retinopathy Deep Learning CNN Image Enhancement CLAHE

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iCVS-Inferring Cardio-Vascular hidden States from physiological signals available at the bedside

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PLoS Computational Biology 2023年第9 September期19卷 e1010835-e1010835页

作者： Tannenbaum, Neta Ravid Gottesman, Omer Assadi, Azadeh Mazwi, Mjaye Shalit, Uri Eytan, Danny Faculty of Data and Decision Science Technion Haifa Israel Faculty of Medicine Technion Haifa Israel Department of Computer Science Brown University ProvidenceRI United States Department of Critical Care Medicine The Hospital for Sick Children Toronto Canada Department of Medicine University of Toronto Toronto Canada Pediatric Intensive Care Unit Rambam Medical Center Haifa Israel

Intensive care medicine is complex and resource-demanding. A critical and common challenge lies in inferring the underlying physiological state of a patient from partially observed data. Specifically for the cardiovascular system, clinicians use observables such as heart rate, arterial and venous blood pressures, as well as findings from the physical examination and ancillary tests to formulate a mental model and estimate hidden variables such as cardiac output, vascular resistance, filling pressures and volumes, and autonomic tone. Then, they use this mental model to derive the causes for instability and choose appropriate interventions. Not only this is a very hard problem due to the nature of the signals, but it also requires expertise and a clinician's ongoing presence at the bedside. Clinical decision support tools based on mechanistic dynamical models offer an appealing solution due to their inherent explainability, corollaries to the clinical mental process, and predictive power. With a translational motivation in mind, we developed iCVS: a simple, with high explanatory power, dynamical mechanistic model to infer hidden cardiovascular states. Full model estimation requires no prior assumptions on physiological parameters except age and weight, and the only inputs are arterial and venous pressure waveforms. iCVS also considers autonomic and non-autonomic modulations. To gain more information without increasing model complexity, both slow and fast timescales of the blood pressure traces are exploited, while the main inference and dynamic evolution are at the longer, clinically relevant, timescale of minutes. iCVS is designed to allow bedside deployment at pediatric and adult intensive care units and for retrospective investigation of cardiovascular mechanisms underlying instability. In this paper, we describe iCVS and inference system in detail, and using a dataset of critically- ill children, we provide initial indications to its ability to identify bleeding,

关键词： Blood pressure

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