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Machine learning on the post-vaccination Covid-19 prediction and on the vaccination sentiment analysis

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AIP Conference Proceedings 2023年第1期2746卷

作者： Amira F. Ahmad N. H. A. M. Zaperi Maharani A. Bakar Norizan Mohamed Nor Azlida Aleng Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Kuala Terengganu Terengganu Malaysia Department of Science Mathematics Universiti Teknologi Malaysia Johor Bharu Johor Malaysia Special Interest Group Modelling and Data Analytics Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Kuala Terengganu Terengganu Malaysia

As the SARS-COV-2 pandemic continues to hit and cause many deaths worldwide, the vaccine has been approved to reduce the adverse effects of the disease. Various vaccines such as Pfizer-BioNTech, AstraZeneca, Sinovac, CanSino, Johnson & Johnson, Moderna, and Sinopharm have been approved by WHO. All countries agree that the vaccination process will significantly reduce the number of Covid-19 infectious. This study discusses the impact of the vaccination over the confirmed, recovered, and deaths cases by using machine learning, particularly the Support Vector Regression (SVR). Based on the data from February 2021 to August 2021, we predict 3 cases of confirmed, recovered, and deaths in Malaysia. This study also looked at the trend of the three cases where no vaccines were introduced in Malaysia, but a restriction called the Movement Controlled Order (MCO) period was applied. Since we found some pros and cons of vaccination in Malaysia, which may affect the confirmed cases, we then use artificial neural networks (ANN) to do sentiment analysis of the social opinion regarding the vaccination in Malaysia. It involves gathering about 1000 tweets-collecting data from the tweets keyword "vaksin Malaysia". The results showed that the accuracy of Covid-19 predictions was above 90% of R2 during the vaccination process and the MCO period, and based on the results of sentiment analysis, and it was shown that about 88% of people tend to accept the vaccine positively compared to 8% who accept negatively.

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data-Mining of In-Situ TEM Experiments: Towards Understanding Nanoscale Fracture

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arXiv 2022年

作者： Steinberger, Dominik Issa, Inas Strobl, Rachel Imrich, Peter J. Kiener, Daniel Sandfeld, Stefan Institute of Mechanics and Fluid Dynamics Freiberg University of Mining and Technology Freiberg Germany Department Materials Science Chair of Materials Physics Montanuniversität Leoben Leoben Austria Forschungszentrum Jülich GmbH Jülich52425 Germany Now at Kompetenzzentrum Automobil- und Industrieelektronik GmbH Europastraße 8 Villach9524 Austria Chair of Materials Data Science and Materials Informatics Faculty 5 – Georesources and Materials Engineering RWTH Aachen University Aachen52056 Germany

The lifetime and performance of any engineering component, from nanoscale sensors to macroscopic structures, are strongly influenced by fracture processes. Fracture itself is a highly localized event;originating at the atomic scale by bond breaking between individual atoms close to the crack tip. These processes, however, interact with defects such as dislocations or grain boundaries and influence phenomena on much larger length scales, ultimately giving rise to macroscopic behavior and engineering-scale fracture properties. This complex interplay is the fundamental reason why identifying the atomistic structural and energetic processes occurring at a crack tip remains a longstanding and still unsolved challenge. We develop a new analysis approach for combining quantitative in-situ observations of nanoscale deformation processes at a crack tip with three-dimensional reconstruction of the dislocation structure and advanced computational analysis to address plasticity and fracture initiation in a ductile metal. Our combinatorial approach reveals details of dislocation nucleation, their interaction process, and the local internal stress state, all of which were previously inaccessible to experiments. This enables us to describe fracture processes based on local crack driving forces on a dislocation level with a high fidelity that paves the way towards a better understanding and control of local failure processes in materials. Copyright © 2022, The Authors. All rights reserved.

关键词： Crack tips

Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

A Deep Reinforcement Learning Approach for the Meal Delivery Problem

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arXiv 2021年

作者： Jahanshahi, Hadi Bozanta, Aysun Cevik, Mucahit Kavuk, Eray Mert Tosun, Ayşe Sonuc, Sibel B. Kosucu, Bilgin Başar, Ayşe Data Science Lab Ryerson University TorontoONM5B 2K3 Canada Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul34467 Turkey Getir Perakende Lojistik A.S. Istanbul34337 Turkey

We consider a meal delivery service fulfilling dynamic customer requests given a set of couriers over the course of a day. A courier's duty is to pick up an order from a restaurant and deliver it to a customer. We model this service as a Markov decision process and use deep reinforcement learning as the solution approach. We experiment with the resulting policies on synthetic and real-world datasets and compare those with the baseline policies. We also examine the courier utilization for different numbers of couriers. In our analysis, we specifically focus on the impact of the limited available resources in the meal delivery problem. Furthermore, we investigate the effect of intelligent order rejection and re-positioning of the couriers. Our numerical experiments show that, by incorporating the geographical locations of the restaurants, customers, and the depot, our model significantly improves the overall service quality as characterized by the expected total reward and the delivery times. Our results present valuable insights on both the courier assignment process and the optimal number of couriers for different order frequencies on a given day. The proposed model also shows a robust performance under a variety of scenarios for real-world implementation. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

The warping window size effects the accuracy of person identification based on keystroke dynamics 19

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The warping window size effects the accuracy of person ident...

19th Conference Information Technologies - Applications and Theory, ITAT 2019

作者： Farou, Zakarya Buza, Krisztian Telekom Innovation Labortories Department of Data Science and Engineering Faculty of Informatics ELTE Eötvös Loránd University Budapest Hungary

1-nearest neighbor (1NN) with Dynamic Time Warping (DTW) distance is a popular time series classification technique. In the last decades, research on DTW aimed to improve its classification accuracy, memory usage, and efficiency. According to a recent study, the appropriate selection of the Warping Window Size (WWS) is crucial for the accuracy of 1NN-DTW. In this work, we consider person identification based on keystroke dynamics as a time series classification task, and we examine whether WWS is crucial in this case. We performed experiments on a real-world dataset containing more than 400 typing sessions obtained from 12 users. In the case of this dataset, each user typed the same fixed text. We found that the aforementioned hypothesis was correct, i.e., the classification accuracy indeed depends on the WWS. Furthermore, according to our observations, in our case, the optimal WWS is less than 3% which is substantially different from 10% that has been used as a default value in various works. Copyright © 2019 for this paper by its authors.

关键词： Dynamics

Simulation of single particle flowing in a microfluidic device using molecular dynamics method 3

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Simulation of single particle flowing in a microfluidic devi...

3rd Annual Scientific Meeting on Medical Physics and Biophysics, PIT-FMB in conjunction with the 17th South-East Asia Congress of Medical Physics, SEACOMP 2019

作者： Viridi, Sparisoma Haryanto, Freddy Anshori, Isa Haekal, Mohammad Department of Physics Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jalan Ganesha 10 Bandung40132 Indonesia Department of Biomedical Engineering School of Electrical Engineering and Informatics Institut Teknologi Bandung Jalan Ganesha 10 Bandung40132 Indonesia Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Sukolilo Surabaya60111 Indonesia

Blood cells are modeled as spherical particles that flow through a microfluidic device with one inlet and two outlet channels, which is designed as a separator of blood particles. Molecular dynamics (MD) method was used intuitively in the simulation with the help of Semi-Circle Segmented Path Generator (SCSPG) as an approximation in creating fluid profile along the device channel. The trajectories generated from SCSPG was advanced using a fully developed Poiseuille flow with maximum fluid speed on the trajectories and the tails of speed distribution which was extended to the size of the channel with speed of zero at the channel walls. It has been observed that for a single particle trajectory the outlet channel was chosen by the particle depends on the axial position of the particles. Mass of particle determines how hard the particle deflects due to fluid profile. A better design is proposed in this work for separating two groups of particles with different size. © Published under licence by IOP Publishing Ltd.

关键词： Trajectories

Deep-Motion-Net: GNN-based volumetric organ shape reconstruction from single-view 2D projections

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arXiv 2024年

作者： Wijesinghe, Isuru Nix, Michael Zakeri, Arezoo Hokmabadi, Alireza Al-Qaisieh, Bashar Gooya, Ali Taylor, Zeike A. Centre for Computational Imaging and Simulation Technologies in Biomedicine School of Mechanical Engineering University of Leeds Leeds United Kingdom Institute of Medical and Biological Engineering School of Mechanical Engineering University of Leeds Leeds United Kingdom Department of Medical Physics and Engineering St James’s University Hospital Leeds Teaching Hospitals NHS Trust Leeds United Kingdom Division of Informatics Imaging and Data Sciences Faculty of Biology Medicine and Health University of Manchester Manchester United Kingdom School of Medicine and Population Health University of Sheffield Sheffield United Kingdom School of Computing Science University of Glasgow Glasgow United Kingdom

We propose Deep-Motion-Net: an end-to-end graph neural network (GNN) architecture that enables 3D (volumetric) organ shape reconstruction from a single in-treatment kV planar X-ray image acquired at any arbitrary projection angle. Estimating and compensating for true anatomical motion during radiotherapy is essential for improving the delivery of planned radiation dose to target volumes while sparing organs-at-risk, and thereby improving the therapeutic ratio. Achieving this using only limited imaging available during irradiation and without the use of surrogate signals or invasive fiducial markers is attractive. The proposed model learns the mesh regression from a patient-specific template and deep features extracted from kV images at arbitrary projection angles. A 2D-CNN encoder extracts image features, and four feature pooling networks fuse these features to the 3D template organ mesh. A ResNet-based graph attention network then deforms the feature-encoded mesh. The model is trained using synthetically generated organ motion instances and corresponding kV images. The latter is generated by deforming a reference CT volume aligned with the template mesh, creating digitally reconstructed radiographs (DRRs) at required projection angles, and DRR-to-kV style transferring with a conditional CycleGAN model. The overall framework was tested quantitatively on synthetic respiratory motion scenarios and qualitatively on in-treatment images acquired over full scan series for liver cancer patients. Overall mean prediction errors for synthetic motion test datasets were 0.16±0.13 mm, 0.18±0.19 mm, 0.22±0.34 mm, and 0.12±0.11 mm. Mean peak prediction errors were 1.39 mm, 1.99 mm, 3.29 mm, and 1.16 mm. © 2024, CC BY-SA.

关键词： Radiotherapy

Probability Distribution on Full Rooted Trees

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arXiv 2021年

作者： Nakahara, Yuta Saito, Shota Kamatsuka, Akira Matsushima, Toshiyasu Center for Data Science Waseda University Tokyo169-8050 Japan Faculty of Informatics Gunma University Gunma371-8510 Japan Dept. of Information Science Shonan Institute of Technology Kanagawa251-8511 Japan Dept. of Applied Math. Waseda University Tokyo169-8555 Japan

The recursive and hierarchical structure of full rooted trees is applicable to represent statistical models in various areas, such as data compression, image processing, and machine learning. In most of these cases, the full rooted tree is not a random variable;as such, model selection to avoid overfitting becomes problematic. A method to solve this problem is to assume a prior distribution on the full rooted trees. This enables the optimal model selection based on the Bayes decision theory. For example, by assigning a low prior probability to a complex model, the maximum a posteriori estimator prevents the selection of the complex one. Furthermore, we can average all the models weighted by their posteriors. In this paper, we propose a probability distribution on a set of full rooted trees. Its parametric representation is suitable for calculating the properties of our distribution using recursive functions, such as the mode, expectation, and posterior distribution. Although such distributions have been proposed in previous studies, they are only applicable to specific applications. Therefore, we extract their mathematically essential components and derive new generalized methods to calculate the expectation, posterior distribution, etc. Copyright © 2021, The Authors. All rights reserved.

关键词： Probability distributions

Emotion recognition in doctor-patient interactions from real-world clinical video database: Initial development of artificial empathy

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Computer Methods and Programs in Biomedicine 2023年 233卷 107480-107480页

作者： Huang, Chih-Wei Wu, Bethany C.Y. Nguyen, Phung Anh Wang, Hsiao-Han Kao, Chih-Chung Lee, Pei-Chen Rahmanti, Annisa Ristya Hsu, Jason C. Yang, Hsuan-Chia Li, Yu-Chuan Jack Taipei Medical University Taipei Taiwan Taipei Medical University Ringgold standard institution - Center for Simulation in Medical Education Taipei 116 Taiwan National Taiwan University Children and Family Research Center Sponsored by CTBC Charity Foundation Taipei Taiwan Clinical Data Center Office of Data Science Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University TMU Da'an Campus 15 F No. 172-1 Kee lung Road Section 2 Da-an District Taipei Taiwan Research Center of Big Data and Meta-analysis Wanfang Hospital Taipei Medical University Taipei Taiwan Department of Dermatology Wanfang Hospital Taipei Medical University Taiwan Department of Dermatology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Delta Research Center Taipei Taiwan Department of Health Policy and Management Faculty of Medicine Public Health and Nursing Universitas Gadjah Mada Yogyakarta Indonesia International PhD Program in Biotech and Healthcare Management College of Management Taipei Medical University Taipei Taiwan Research Center of Data Science on Healthcare Industry College of Management Taipei Medical University Taipei Taiwan

Background and objective: The promising use of artificial intelligence (AI) to emulate human empathy may help a physician engage with a more empathic doctor-patient relationship. This study demonstrates the application of artificial empathy based on facial emotion recognition to evaluate doctor-patient relationships in clinical practice. Methods: A prospective study used recorded video data of doctor-patient clinical encounters in dermatology outpatient clinics, Taipei Municipal Wanfang Hospital, and Taipei Medical University Hospital collected from March to December 2019. Two cameras recorded the facial expressions of four doctors and 348 adult patients during regular clinical practice. Facial emotion recognition was used to analyze the basic emotions of doctors and patients with a temporal resolution of 1 second. In addition, a physician-patient satisfaction questionnaire was administered after each clinical session, and two standard patients gave impartial feedback to avoid bias. Results: data from 326 clinical session videos showed that (1) Doctors expressed more emotions than patients (t [326] > = 2.998, p © 2023

关键词： Emotion Recognition