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4th International Conference on Informatics and Computing, ICIC 2019

作者： Afrizal, Sandra Hakiem Hidayanto, Achmad Nizar Hakiem, Nashrul Sartono, Arief Priyambodo, Seno Eryando, Tris Faculty of Public Health Universitas Indonesia Depok Indonesia Faculty of Computer Science Universitas Indonesia Depok Indonesia Faculty of Science and Technology Universitas Islam Negeri Syarif Hidayatullah Jakarta Indonesia Laboratory for Information and Communication Technology Services Agency for the Assessment and Application of Technology Indonesia

ISBN: (纸本)9781728122076

Antenatal Care (ANC) service performed by midwives in Primary Health Care (PHC) became more efficient through mobile health (mHealth) implementation. However, no work has yet been conducted toward the development of the technologies for integrating the service since midwives provide not only in-house clinic service but also community works such as home visit and site clinic. The objective of the study was to design a mobile health application using user-centered needs approach to consider the functionality and usability of the users. We conducted a case study in South Tangerang, an urban city of Banten Province, Indonesia using in-depth interview for prospective users and collected usability testing of mock-up mobile application through System Usability Scale (SUS) among 20 respondents. Functional features were identified namely: 1) pregnancy register such as personal identity, spouse identity, and ANC content, 2) pregnancy monitoring such as high-risk pregnancy, diagnosis, and maternal status. As conclusion, this is the first study to design a mobile Health for integrating ANC service based on UCD framework. The study indicates that UCD is found as useful guidance for designing a monitoring and registration mHealth application named SIMORI for integrating ANC service in primary health care. © 2019 IEEE.

关键词： mHealth

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S-shaped binary whale optimization algorithm for feature selection 1st

S-shaped binary whale optimization algorithm for feature sel...

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1st International Symposium on Signal and Image Processing, ISSIP 2017

作者： Hussien, Abdelazim G. Hassanien, Aboul Ella Houssein, Essam H. Bhattacharyya, Siddhartha Amin, Mohamed Faculty of Science Fayoum University Faiyum Egypt Faculty of Computers and Information Cairo University Giza Egypt Faculty of Computers and Information Minia University Minya Egypt Department of Computer Application RCC Institute of Information Technology Kolkata700 015 India Faculty of Science Menoufia University Shibin El Kom Egypt

ISBN: (纸本)9789811088629

Whale optimization algorithm is one of the recent nature-inspired optimization technique based on the behavior of bubble-net hunting strategy. In this paper, a novel binary version of whale optimization algorithm (bWOA) is proposed to select the optimal feature subset for dimensionality reduction and classifications problem. The new approach is based on a sigmoid transfer function (S-shape). By dealing with the feature selection problem, a free position of the whale must be transformed to their corresponding binary solutions. This transformation is performed by applying an S-shaped transfer function in every dimension that defines the probability of transforming the position vectors’ elements from 0 to 1 and vice versa and hence force the search agents to move in a binary space. K-NN classifier is applied to ensure that the selected features are the relevant ones. A set of criteria are used to evaluate and compare the proposed bWOA-S with the native one over eleven different datasets. The results proved that the new algorithm has a significant performance in finding the optimal feature. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Classification (of information)

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Deep Learning Assisted Raman Spectroscopy for Rapid Identification of 2D Materials

arXiv

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arXiv 2023年

作者： Qi, Yaping Hu, Dan Wu, Zhenping Zheng, Ming Cheng, Guanghui Jiang, Yucheng Chen, Yong P. Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Av. Wai Long 999078 China Tohoku University Sendai980-8577 Japan State Key Laboratory of Information Photonics and Optical Communications School of Science Beijing University of Posts and Telecommunications Beijing100876 China School of Materials Science and Physics China University of Mining and Technology Xuzhou221116 China Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and Technology Suzhou University of Science and Technology Jiangsu Suzhou215009 China Department of Physics and Astronomy Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Institute of Physics and Astronomy Villum Center for Hybrid Quantum Materials and Devices Aarhus University AarhusC 8000 Denmark

Two-dimensional (2D) materials have attracted extensive attention due to their unique characteristics and application potentials. Raman spectroscopy, as a rapid and non-destructive probe, exhibits distinct features and holds notable advantages in the structural characterization of 2D materials. However, traditional data analysis of Raman spectra relies on manual interpretation and feature extraction, which are both time-consuming and subjective. In this work, we employ deep learning techniques, including classificatory and generative deep learning, to assist the analysis of Raman spectra of typical 2D materials. For the limited and unevenly distributed Raman spectral data, we propose a data augmentation approach based on Denoising Diffusion Probabilistic Models (DDPM) to augment the training dataset and construct a four-layer Convolutional Neural Network (CNN) for 2D material classification. Experimental results illustrate the effectiveness of DDPM in addressing data limitations and significantly improved classification model performance. The proposed DDPM-CNN method shows high reliability, with 100%classification accuracy. Our work demonstrates the practicality of deep learning-assisted Raman spectroscopy for high-precision recognition and classification of 2D materials, offering a promising avenue for rapid and automated spectral analysis. © 2023, CC BY.

关键词： Raman spectroscopy

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Data-Efficient Federated Semi-Supervised Learning Framework Via Pseudo Supervision Refinement Strategy for Lung Tumor Segmentation

SSRN

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SSRN 2024年

作者： Shi, Zhenwei Li, Weixing Pan, Xipeng Zhang, Zhen Wu, Guangyao Ye, Guanchao Wee, Leonard Dekker, Andre Han, Chu Shi, Lei Liu, Zaiyi Liu, Zhenbing School of Computer and Information Security Guilin University of Electronic Technology Guilin541004 China Department of Radiology Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Southern Medical University Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China Chinese Academy of Sciences Zhejiang Hangzhou310022 China Chinese Academy of Sciences Zhejiang Hangzhou310022 China Department of Radiology Huazhong University of Science and Technology Tongji Medical College Union Hospital Hubei Wuhan430021 China Clinical Data Science Faculty of Health Medicine Life Sciences Maastricht University Maastricht6229 ET Netherlands GROW School of Oncology and Reproduction Maastricht University Medical Centre+ Maastricht6229 ET Netherlands Guangdong Provincial People's Hospital China

Background and Objective: Accurate lung tumor segmentation is crucial for early clinical diagnosis and subsequent treatment planning for patients. Conducting centralized training to enhance model performance is impractical in real-world scenarios. Although federated learning offers a promising solution to this issue, effectively and efficiently utilizing unlabeled data remains a challenge. Methods: In this work, we propose a novel federated semi-supervised learning framework (FSSL) for complex and realistic scenario, which can efficiently leverage the limited labeled data and abundant unlabeled data from multiple clinical sites. Specifically, we initially conduct federated self-supervised pre-training at completely unlabeled sites using masked image modeling strategy. Subsequently, in the fine-tuning stage, we propose a pseudo supervision refinement strategy to leverage the unlabeled data from partially labeled sites, which not only reduces pseudo label noise but also helps stabilize the training process. Moreover, we propose a dynamic model aggregation strategy to assist the server in dynamically combining all local models during communication round. Results: We conducted extensive comparative and ablation experiments to validate the effectiveness of our proposed method. When only a limited amount of labeled data is available in the partially labeled sites, even without loading the pre-trained model, our approach consistently achieves the highest average Dice scores of 0.7383, 0.4768, and 0.6629 on the internal test sets, external unseen test sets, and pre-training test sets, respectively. Upon loading the pre-trained model, the scores on the three test sets further improve to 0.8205, 0.6185, and 0.7334, respectively. Conclusion: Our proposed method is capable of collaboratively training a global model with robust segmentation performance and generalizability by efficiently utilizing unlabeled data from different sites, even when only a limited amount of labeled da

关键词： Federated learning

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Data-Efficient Federated Semi-Supervised Learning Framework Via Pseudo Supervision Refinement Strategy for Lung Tumor Segmentation

SSRN

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SSRN 2024年

作者： Shi, Zhenwei Li, Weixing Pan, Xipeng Zhang, Zhen Wu, Guangyao Ye, Guanchao Wee, Leonard Andre, Dekker Han, Chu Shi, Lei Liu, Zaiyi Liu, Zhenbing School of Computer and Information Security Guilin University of Electronic Technology Guilin541004 China Department of Radiology Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Southern Medical University Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China Chinese Academy of Sciences Zhejiang Hangzhou310022 China Chinese Academy of Sciences Zhejiang Hangzhou310022 China Department of Radiology Huazhong University of Science and Technology Tongji Medical College Union Hospital Hubei Wuhan430021 China Clinical Data Science Faculty of Health Medicine Life Sciences Maastricht University Maastricht6229 ET Netherlands GROW School of Oncology and Reproduction Maastricht University Medical Centre+ Maastricht6229 ET Netherlands Guangdong Provincial People's Hospital China

Accurate lung tumor segmentation is crucial for early clinical diagnosis and subsequent treatment planning for patients. Centralized model training faces privacy constraints, although federated learning offers a promising solution to this issue, effectively and efficiently utilizing unlabeled data remains a challenge. In this work, we propose a novel federated semi-supervised learning framework for complex and realistic scenario, which can efficiently leverage the limited labeled data and more homogeneous unlabeled data from multiple clinical sites for collaborative model training. Specifically, we initially conduct federated self-supervised pre-training at completely unlabeled sites using masked image modeling strategy. Subsequently, in the fine-tuning stage, we propose a pseudo supervision refinement strategy to leverage the unlabeled data from partially labeled sites, which not only reduces pseudo label noise but also helps stabilize the training process. Moreover, we propose a dynamic model aggregation strategy to assist the server in dynamically combining local models during each communication round to enhance the robustness and generalizability of the global model. Extensive comparative and ablation experiments confirm the efficacy of our proposed methodology, demonstrating improvement over recent state-of-the-art federated semi-supervised segmentation methods in scenarios with only a small amount of labeled data. This approach highlights the significant potential of multi-source and heterogeneous unlabeled data in facilitating the training of federated deep learning models and promotes clinical applications, assists radiologists in diagnosis and improves patient outcomes. The source code will be released on https://***/zhenweishi/FedPSR © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Quantum-Inspired Bat Optimization Algorithm for Automatic Clustering of Grayscale Images 1

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2nd International Symposium on Signal and Image Processing, ISSIP 2018

作者： Dey, Alokananda Bhattacharyya, Siddhartha Dey, Sandip Platos, Jan Snasel, Vaclav Department of Computer Application RCC Institute of Information Technology KolkataWest Bengal India Faculty of Electrical Engineering and Computer Science VSB Technical University of Ostrava Ostrava Czech Republic Department of Computer Science and Engineering OmDayal Group of Institutions Howrah India

ISBN: (数字)9789811367830

ISBN: (纸本)9789811367823

This paper introduces a quantum-inspired framework with the bat optimization algorithm for automatic clustering of image datasets. The aim of this work is to find out the optimal number of clusters from an image dataset on a run. A comparison has been produced between the quantum-inspired bat optimization algorithm and its classical counterpart. As a result, it is seen that the quantum-inspired version outperforms its classical counterpart. Computational experiments have been conducted on four Berkeley image datasets. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Clustering algorithms

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applications of Raman Spectroscopy in Clinical Medicine

arXiv

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arXiv 2023年

作者： Qi, Yaping Chen, Esther Xinyi Hu, Dan Yang, Ying Wu, Zhenping Zheng, Ming Sadi, Mohammad A. Jiang, Yucheng Zhang, Kang Chen, Zi Chen, Yong P. Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Av. Wai Long China Tohoku University Sendai980-8577 Japan Faculty of Medicine Macau University of Science and Technology Av. Wai Long China State Key Laboratory of Information Photonics and Optical Communications School of Science Beijing University of Posts and Telecommunications Beijing100876 China School of Materials Science and Physics China University of Mining and Technology Xuzhou221116 China Department of Physics and Astronomy Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and Technology Suzhou University of Science and Technology Jiangsu Suzhou215009 China Division of Thoracic Surgery Brigham and Women's Hospital Harvard Medical School 75 Francis Street BostonMA02115 United States Institute of Physics and Astronomy Villum Center for Hybrid Quantum Materials and Devices Aarhus University AarhusC 8000 Denmark

Raman spectroscopy provides spectral information related to the specific molecular structures of substances and has been well established as a powerful tool for studying biological tissues and diagnosing diseases. This article reviews recent advances in Raman spectroscopy and its applications in diagnosing various critical diseases, including cancers, infections, and neurodegenerative diseases, and in predicting surgical outcomes. These advances are explored through discussion of state-of-the-art forms of Raman spectroscopy, such as surface-enhanced Raman spectroscopy, resonance Raman spectroscopy, and tip-enhanced Raman spectroscopy employed in biomedical sciences. We discuss biomedical applications, including various aspects and methods of ex vivo and in vivo medical diagnosis, sample collection, data processing, and achievements in realizing the correlation between Raman spectra and biochemical information in certain diseases. Finally, we present the limitations of the current study and provide perspectives for future research. © 2023, CC BY.

关键词： COVID-19

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A DDPG Hybrid with Graph Scheme for Resource Management in Digital Twin-assisted Biomedical Cyber-Physical Systems

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IEEE Transactions on Network and Service Management 2025年

作者： Xu, Yi-Han Ding, Rong-Xing Xu, Xiao-Ren Zhou, Ding Zhou, Wen Qinghai University School of Computer Technology and Application Xining810016 China Nanjing Forestry University College of Information Science and Technology and Artificial Intelligence Nanjing210037 China The University of New South Wales School of Electrical Engineering and Telecommunications Sydney2052 Australia Universiti Malaya Faculty of Engineering Kuala Lumpur50603 Malaysia Guangzhou Maritime University College of Low Altitude Equipment and Intelligent Control Guangzhou510725 China

In this paper, we focus on the resource optimization issue in Wireless Body Area Networks (WBANs) section of a novel Biomedical Cyber Physical System (BM-CPS) in which the Digital Twin (DT) technique is adopted concurrently. We propose a scenario where multiple physiological sensor nodes continuously monitor the electrophysiology signals from patients and connect to a Cyber Managing Center (Cyb-MC) in a wireless way to transmit the physiological indices to the paramedic reliably. Specifically, to optimize the energy efficiency of physiological sensors while ensuring the reliability of emergency-critical electrophysiology signals transmissions and modeling the uncertain stochastic environments, the optimization issue is transformed into a Markov Decision Process (MDP) in terms of joint transmission mode, relay assignment, transmission power and time slot scheduling consideration. Subsequently, we propose a Random Graph-enabled Deep Deterministic Policy Gradient (RG-DDPG) scheme to tackle the challenge while releasing computing complexity. In addition, as an innovative paradigm for reshaping cyberspace applications, the DT of WBAN is created to capture the time-varying resource status, where virtualized and intelligent resource management can be performed uniformly. Finally, extensive simulation studies verified the advancement of the proposed scheme and demonstrated that the DT can mirror the topology, predict the behavior, and administrate the resources of the WBANs. © 2004-2012 IEEE.

关键词： Resource allocation

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A multi-view approach for predicting microbedisease associations by fusing the linear and nonlinear features

A multi-view approach for predicting microbedisease associat...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ming Liu Wei Dai Wei Peng Yu Fu Yi Pan Faculty of Information Engineering and Automation Kunming University of Science and Technology Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Kunming China Medical school Kunming University of Science and Technology Kunming China Georgia State University Atlanta GA USA

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Microbes play a crucial role in human health and disease. Understanding the relationship between microbes and diseases is conducive to the treatment and diagnosis of diseases. Recently, many computational methods have been proposed to predict disease-microbe associations. However, most of the existing methods only consider a single model and explore the disease-microbe associations from a single view. To improve the prediction accuracy, we propose a novel multi-view approach that fuses the linear and nonlinear features to predict new potential associations between diseases and microbes. We first design a non-negative matrix tri-factorization method to extract the linear features of diseases and microbes. We input the linear features from the non-negative matrix tri-factorization model and bi-random walk model into a capsule neural network to obtain the diseases and microbes' nonlinear features. Finally, we leverage a logistic regression model to combine the non-negative matrix tri-factorization model predictions, bi-random walk model predictions and the capsule neural network predictions to obtain the final association scores between microbes and diseases. We apply our method to predict human microbedisease associations. Experimental results show that our fusion model outperforms the non-negative matrix tri-factorization model, bi-random walk model and other existing models.

关键词： Diseases Predictive models Neural networks Feature extraction Computational modeling Logistics Kernel

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Estimating red noise in quasi-periodic signals with MCMC-based Bayesian

arXiv

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arXiv 2020年

作者： Liang, Bo Meng, Yao Feng, Song Yang, Yunfei Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Key Laboratory of Solar Activity National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China Yunnan Key Laboratory of Computer Technology Application Kunming650500 China

Multi-parameter Bayesian inferences based on Markov chain Monte Carlo (MCMC) samples have been widely used to estimate red noise in solar period-periodic signals. To MCMC, proper priors and sufficient iterations are prerequisites ensuring the accuracy of red noise estimation. We used MCMC-based Bayesian inferences to estimate 100 groups of red noise synthesized randomly for evaluating its accuracy. At the same time, the Brooks-Gelman algorithm was employed to precisely diagnose the convergence of the Markov chains generated by MCMC. The root-mean-square error of parameter inferences to the synthetic data is only 1.14. Furthermore, we applied the algorithm to analyze the oscillation modes in a sunspot and a flare. A 70 s period is detected in the sunspot umbra in addition to 3- and 5-minute periods, and a 40 s period is detected in the flare. The results prove that estimating red noise with MCMC-based Bayesian has more high accuracy in the case of proper priors and convergence. We also find that the number of iterations increases dramatically to achieve convergence as the number of parameters grows. Therefore, we strongly recommend that when estimating red noise with MCMC-based Bayesian, different initial values must be selected to ensure that the entire posterior distribution is covered. Copyright © 2020, The Authors. All rights reserved.

关键词： Bayesian networks

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