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Heart failure prediction using machine learning technique

AIP Conference Proceedings 2023年第1期2608卷

作者： Mohammad Naser Hasan Radaideh Hiam Alquran Wan Azani Mustafa Mohd Aminudin Jamlos Syed Zulkarnain Syed Idrus Department of Biomedical Engineering Jordan University of Science and Technology Al-Ramtha Jordan Biomedical Systems and Medical Informatics Engineering Yarmouk University 21163 Irbid Jordan Faculty of Electrical Engineering Technology University of Malaysia Perlis UniCITI Alam Campus Sungai Chuchuh 02100 Padang Besar Perlis Malaysia Advanced Computing (AdvComp) Centre of Excellence (CoE) Universiti Malaysia Perlis 02600 Arau Perlis Malaysia Faculty of Electronic Engineering Technology University of Malaysia Perlis UniCITI Alam Campus Sungai Chuchuh 02100 Padang Besar Perlis Malaysia Faculty of Applied and Human Sciences Universiti Malaysia Perlis 01000 Kangar Perlis Malaysia

Heart failure is one of the familiar diseases that needs a special care and its prediction in early stage helps to take caution of its symptoms. By the invention of machine learning, some tasks become easier to handle like expectations, classification and clusstering. The main goal of this article is to predict heart failure using the patient record. This paper focus on building more than one model to predict the prescne or absence of heart failure. These models aim to reduce the risk of death and to make the life is more adaptive with this non-cureable disease. The three models are built with different number of features. The comparisin between these models is evaluated. The first model is build with 12 features and it achived the highest accurcy 84.6% in Bagged Tree classifier, whereas the second model is exploited with 6 features, its accuracy reduced to 75.6% Cosine KNN classifier. The last model is performed around 75.9 % with just two features. This study recommend s to use the whole record fetures to obtain the highest accuracy for predicting heart failar cases.

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Generating 3D TOF-MRA volumes and segmentation labels using generative adversarial networks

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Medical Image Analysis 2022年 78卷 102396-102396页

作者： Subramaniam, Pooja Kossen, Tabea Ritter, Kerstin Hennemuth, Anja Hildebrand, Kristian Hilbert, Adam Sobesky, Jan Livne, Michelle Galinovic, Ivana Khalil, Ahmed A. Fiebach, Jochen B. Frey, Dietmar Madai, Vince I. CLAIM - Charité Lab for AI in Medicine Charité Universitätsmedizin Berlin Germany Department of Computer Engineering and Microelectronics Computer Vision & Remote Sensing Technical University Berlin Berlin Germany Berlin Germany Bernstein Center for Computational Neuroscience Berlin Germany Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine Charité Universitätsmedizin Berlin Berlin Germany Fraunhofer MEVIS Max-von-Laue-Str. 2 Bremen Germany Department VI Computer Science and Media Beuth University of Applied Sciences Berlin Germany Johanna-Etienne-Hospital Neuss Germany Centre for Stroke Research Berlin Charité Universitätsmedizin Berlin Berlin Germany Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany Mind Brain Body Institute Berlin School of Mind and Brain Humboldt University Berlin Berlin Germany Berlin Institute of Health Berlin Germany School of Computing and Digital Technology Faculty of Computing Engineering and the Built Environment Birmingham City University Birmingham United Kingdom QUEST-Center for Transforming Biomedical Research Berlin Institute of Health Charité Universitätsmedizin Berlin Charitéplatz 1 Berlin10117 Germany

Deep learning requires large labeled datasets that are difficult to gather in medical imaging due to data privacy issues and time-consuming manual labeling. Generative Adversarial Networks (GANs) can alleviate these challenges enabling synthesis of shareable data. While 2D GANs have been used to generate 2D images with their corresponding labels, they cannot capture the volumetric information of 3D medical imaging. 3D GANs are more suitable for this and have been used to generate 3D volumes but not their corresponding labels. One reason might be that synthesizing 3D volumes is challenging owing to computational limitations. In this work, we present 3D GANs for the generation of 3D medical image volumes with corresponding labels applying mixed precision to alleviate computational constraints. We generated 3D Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) patches with their corresponding brain blood vessel segmentation labels. We used four variants of 3D Wasserstein GAN (WGAN) with: 1) gradient penalty (GP), 2) GP with spectral normalization (SN), 3) SN with mixed precision (SN-MP), and 4) SN-MP with double filters per layer (c-SN-MP). The generated patches were quantitatively evaluated using the Fréchet Inception Distance (FID) and Precision and Recall of Distributions (PRD). Further, 3D U-Nets were trained with patch-label pairs from different WGAN models and their performance was compared to the performance of a benchmark U-Net trained on real data. The segmentation performance of all U-Net models was assessed using Dice Similarity Coefficient (DSC) and balanced Average Hausdorff Distance (bAVD) for a) all vessels, and b) intracranial vessels only. Our results show that patches generated with WGAN models using mixed precision (SN-MP and c-SN-MP) yielded the lowest FID scores and the best PRD curves. Among the 3D U-Nets trained with synthetic patch-label pairs, c-SN-MP pairs achieved the highest DSC (0.841) and lowest bAVD (0.508) compared to the benchmark

关键词： Generative adversarial networks

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A review of detection and classification cervical cell images

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AIP Conference Proceedings 2023年第1期2608卷

作者： Nadzirah Nahrawi Wan Azani Mustafa Syed Zulkarnain Syed Idrus Mohd Aminudin Jamlos Shahrina Ismail Hiam Alquran Ali Mohammad Alqudah Faculty of Electrical Engineering Technology Universiti Malaysia Perlis (UniMAP) Campus Pauh Putra 02600 Arau Perlis Malaysia Advanced Computing (AdvComp) Centre of Excellence (CoE) Universiti Malaysia Perlis (UniMAP) Campus Pauh Putra 02600 Arau Perlis Malaysia Faculty of Applied and Human Sciences Universiti Malaysia Perlis (UniMAP) Jalan Alor Setar-Kangar 01000 Kangar Perlis Malaysia Faculty of Electronic Engineering Technology University of Malaysia Perlis Campus Pauh Putra 02600 Arau Perlis Malaysia Faculty of Science and Technology Universiti Sains Islam Malaysia (USIM) Bandar Baru Nilai 71800 Negeri Sembilan Malaysia Department of Biomedical Systems and Informatics Engineering Yarmouk University Irbid 21163 Jordan

Cervical cancer is a very prevalent disease among women all over the world. Cervical cancer can form in the cervix cells found in the lower uterus. Women all over the world are at death risk as a result of this type of cancer. Cervical cancer has seven stages: normal intermediate, normal superficial, columnar, mild dysplasia, moderate dysplasia, severe dysplasia, and carcinoma in situ. Doctors in hospitals find it difficult to recognise cancer cells as it is challenging to view a nucleus through the naked eye. A normal cell’s nucleus is smaller than an abnormal cell’s nucleus. It is possible to calculate the size of the abnormal nucleus with the naked eye in order to assess the stages of cervical cancer. A tool for identifying and quantifying Pap smear cell images to detect cervical cancer has recently been suggested by several researchers. This method has the potential to increase detection and classification precision, resulting in improved results with balanced data and samples. A comprehensive study of nucleus detection cervical cancer classification techniques was conducted in this paper. As a result of the findings, the function database, detection and classification process, and device performance were all investigated for further evaluation.

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Nanomechanical behavior of pentagraphyne-based single-layer and nanotubes through reactive classical molecular dynamics

arXiv

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arXiv 2023年

作者： De Sousa, J.M. Brandão, W.H.S. Silva, W.L.A.P. Ribeiro, L.A. Galvão, D.S. Pereira Júnior, M.L. Instituto Federal de Educação Ciência e Tecnologia do Piauí - IFPI Primavera Piauí São Raimundo Nonato64770-000 Brazil Applied Physics Department "Gleb Wataghin" Institute of Physics - IFGW University of Campinas - UNICAMP Cidade Universitária Rua Sérgio Buarque de Holanda São Paulo Campinas13083-859 Brazil Department of Physics Federal University of Piauí - UFPI Ininga Piauí Teresina64049-550 Brazil University of Brasília Faculty UnB Planaltina PPGCIMA Brasília73345-010 Brazil University of Brasília Institute of Physics Brasília70910-900 Brazil Center for Computing in Engineering and Sciences University of Campinas Cidade Universitária Campinas Rua Sérgio Buarque de Holanda 777 São Paulo13083-859 Brazil University of Brasília Faculty of Technology Department of Electrical Engineering Brasília70910-900 Brazil

In a recent theoretical study, a new 2D carbon allotrope called pentagraphyne (PG-yne) was proposed. This allotrope is derived from pentagraphene by introducing acetylenic linkages between sp3 and sp2 hybridized carbon atoms. Due to its interesting electronic and structural properties, it is of interest to investigate the mechanical behavior of PG-yne in both monolayer and nanotube topologies. To achieve this, we performed fully atomistic reactive (ReaxFF) molecular dynamics simulations, and our results show that Young's modulus average of PG-yne monolayers is approximately 913 GPa, at room temperature. In comparison, it ranges from 497-789 GPa for the nanotubes studied. Furthermore, we observed that PG-yne monolayers exhibit a direct transition from elastic to complete fracture under critical strain without a plastic regime. In contrast, some PG-yne nanotubes exhibit an extended flat plastic regime before total *** Codes 00-XX Copyright © 2023, The Authors. All rights reserved.

关键词： Molecular dynamics

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The putative center in NGC 1052

arXiv

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arXiv 2025年

作者： Baczko, Anne-Kathrin Kadler, Matthias Ros, Eduardo Fromm, Christian M. Wielgus, Maciek Perucho, Manel Krichbaum, Thomas P. Baloković, Mislav Blackburn, Lindy Chan, Chi-Kwan Issaoun, Sara Janssen, Michael Ricci, Luca Akiyama, Kazunori Albentosa-Ruíz, Ezequiel Alberdi, Antxon Alef, Walter Algaba, Juan Carlos Anantua, Richard Asada, Keiichi Azulay, Rebecca Bach, Uwe Ball, David Bandyopadhyay, Bidisha Barrett, John Bauböck, Michi Benson, Bradford A. Bintley, Dan Blundell, Raymond Bouman, Katherine L. Bower, Geoffrey C. Boyce, Hope Bremer, Michael Brinkerink, Christiaan D. Brissenden, Roger Britzen, Silke Broderick, Avery E. Broguiere, Dominique Bronzwaer, Thomas Bustamante, Sandra Byun, Do-Young Carlstrom, John E. Ceccobello, Chiara Chael, Andrew Chang, Dominic O. Chatterjee, Koushik Chatterjee, Shami Chen, Ming-Tang Chen, Yongjun Cheng, Xiaopeng Cho, Ilje Christian, Pierre Conroy, Nicholas S. Conway, John E. Cordes, James M. Crawford, Thomas M. Crew, Geoffrey B. Cruz-Osorio, Alejandro Cui, Yuzhu Dahale, Rohan Davelaar, Jordy De Laurentis, Mariafelicia Deane, Roger Dempsey, Jessica Desvignes, Gregory Dexter, Jason Dhruv, Vedant Dihingia, Indu K. Doeleman, Sheperd S. Dougall, Sean Taylor Dzib, Sergio A. Eatough, Ralph P. Emami, Razieh Falcke, Heino Farah, Joseph Fish, Vincent L. Fomalont, Edward Ford, H. Alyson Foschi, Marianna Fraga-Encinas, Raquel Freeman, William T. Friberg, Per Fuentes, Antonio Galison, Peter Gammie, Charles F. García, Roberto Gentaz, Olivier Georgiev, Boris Goddi, Ciriaco Gold, Roman Gómez-Ruiz, Arturo I. Gómez, José L. Gu, Minfeng Gurwell, Mark Hada, Kazuhiro Haggard, Daryl Haworth, Kari Hecht, Michael H. Hesper, Ronald Heumann, Dirk Ho, Luis C. Ho, Paul Honma, Mareki Huang, Chih-Wei L. Huang, Lei Hughes, David H. Impellizzeri, C.M. Violette Inoue, Makoto James, David J. Jannuzi, Buell T. Jeter, Britton Jiang, Wu Jiménez-Rosales, Alejandra Johnson, Michael D. Jorstad, Svetlana Joshi, Abhishek V. Jung, Taehyun Karami, Mansour Karuppusamy, Ramesh Kawashima, Tomohisa Keating, Garrett K. Kettenis, M Department of Space Earth and Environment Chalmers University of Technology GothenburgSE-41296 Sweden Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany Institut für Theoretische Physik und Astrophysik Universität Würzburg Emil-Fischer-Str. 31 WürzburgD-97074 Germany Institut für Theoretische Physik Goethe-Universität Frankfurt Max-von-Laue-Straße 1 Frankfurt am MainD-60438 Germany Departament d'Astronomia i Astrofísica Universitat de València C. Dr. Moliner 50 Burjassot ValènciaE-46100 Spain Observatori Astronòmic Universitat de València C. Catedrático José Beltrán 2 Paterna ValènciaE-46980 Spain Yale Center for Astronomy & Astrophysics Yale University 52 Hillhouse Avenue New HavenCT06511 United States Black Hole Initiative Harvard University 20 Garden Street CambridgeMA02138 United States Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States Steward Observatory Department of Astronomy University of Arizona 933 N. Cherry Ave. TucsonAZ85721 United States Data Science Institute University of Arizona 1230 N. Cherry Ave. TucsonAZ85721 United States Program in Applied Mathematics University of Arizona 617 N. Santa Rita TucsonAZ85721 United States Radboud University PO Box 9010 Nijmegen6500 GL Netherlands Massachusetts Institute of Technology Haystack Observatory 99 Millstone Road WestfordMA01886 United States National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n GranadaE-18008 Spain Department of Physics Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia Department of Physics & Astronomy The University of Texas at San Antonio One UTSA Circle San AntonioTX78249 United States Institute of Astronomy and Astrophysics Academia Sinica 11F of Astronomy-Mathematics Building AS/NTU No. 1 Sec. 4 Roosevelt Rd. Taipei10617 Taiwan Astronomy Depart

Context. Many active galaxies harbor powerful relativistic jets, however, the detailed mechanisms of their formation and acceleration remain poorly understood. Aims. To investigate the area of jet acceleration and collimation with the highest available angular resolution, we study the innermost region of the bipolar jet in the nearby low-ionization nuclear emission-line region (LINER) galaxy NGC 1052. Methods. We combined observations of NGC 1052 taken with VLBA, GMVA, and EHT over one week in the spring of 2017. Our study is focused on the size and continuum spectrum of the innermost region containing the central engine and the footpoints of both jets. We employed a synchrotron-self absorption model to fit the continuum radio spectrum and we combined the size measurements from close to the central engine out to ∼1 pc to study the jet collimation. Results. For the first time, NGC 1052 was detected with the EHT, providing a size of the central region in-between both jet bases of 43 µas perpendicular to the jet axes, corresponding to just around 250 RS (Schwarzschild radii). This size estimate supports previous studies of the jets expansion profile which suggest two breaks of the profile at around 3 × 103 RS and 1 × 104 RS distances to the core. Furthermore, we estimated the magnetic field to be 1.25 Gauss at a distance of 22 µas from the central engine by fitting a synchrotron-self absorption spectrum to the innermost emission feature, which shows a spectral turn-over at ∼130 GHz. Assuming a purely poloidal magnetic field, this implies an upper limit on the magnetic field strength at the event horizon of 2.6 × 104 Gauss, which is consistent with previous measurements. Conclusions. The complex, low-brightness, double-sided jet structure in NGC 1052 makes it a challenge to detect the source at millimeter (mm) wavelengths. However, our first EHT observations have demonstrated that detection is possible up to at least 230 GHz. This study offers a glimpse through the dens

关键词： Synchrotrons

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Linear Programming Model for Investment Problem in Maximizing the Total Return

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Journal of Physics: Conference Series 2021年第1期1988卷

作者： Syarifah Zyurina Nordin Farhana Johar Noratikah Abu Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia 54100 Kuala Lumpur Wilayah Persekutuan Kuala Lumpur Malaysia Mathematical Sciences Department Faculty of Science Universiti Teknologi Malaysia 81310 Johor Bahru Johor Malaysia Centre for Matematical Sciences College of Computing and Applied Sciences Universiti Malaysia Pahang 26300 Gambang Kuantan Pahang Malaysia

In this paper, we concentrate on the investment problem of fixed deposit (FD). Our problem is to allocate the amount invested to a suitable tenure and obtain the optimal investment. There are two types of maturity that need to be considered which are short-term and long-term. Our objective is to maximize the total return of the total amount invested with a different percentage of annual return. A linear programming (LP) model is proposed to solve this investment problem using scheduling methodology. We conduct a computational experiment of a real case study for one company located in Kuala Lumpur with RM 20.6 million of investment to see the performance of the model by using the Excel Solver Parameter package. The results show that a significant improvement obtains by our model compared to the original investment practice by the company.

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Corrigendum to “A hybrid algorithm (BAPSO) for capacity configuration optimization in a distributed solar PV based microgrid” [Energy Rep. 7 (2021) 7906–7912]

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Energy Reports 2024年 12卷 5757-5757页

作者： Ahmad Almadhor Hafiz Tayyab Rauf Muhammad Attique Khan Seifedine Kadry Yunyoung Nam Department of Computer Engineering and Networks Jouf University Saudi Arabia Centre for Smart Systems AI and Cybersecurity Staffordshire University Stoke-on-Trent United Kingdom Department of Computer Science HITEC University Taxila Pakistan Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway Department of Computer Science and Engineering Soonchunhyang University Asan South Korea

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Accuracy and Convergence Analysis of uFA-FastSLAM for Robot and Landmarks Position Estimation

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Journal of Physics: Conference Series 2021年第1期2129卷

作者： R J Musridho H Hasan H Haron D Gusman M A Mohammad Department of Informatics Engineering Faculty of Engineering Universitas Pahlawan Tuanku Tambusai Bangkinang Kota 28412 Indonesia. School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru 81310 Malaysia. Applied Industrial Analytics Research Group Johor Bahru 81310 Malaysia. iKohza Software Engineering for Industrial Revolution Malaysia-Japan International Institute of Technology (MJIIT) Universiti Teknologi Malaysia Kuala Lumpur 54100 Malaysia.

In autonomous mobile robots, Simultaneous Localization and Mapping (SLAM) is a demanding and vital topic. One of two primary solutions of SLAM problem is FastSLAM. In terms of accuracy and convergence, FastSLAM is known to degenerate over time. Previous work has hybridized FastSLAM with a modified Firefly Algorithm (FA), called unranked Firefly Algorithm (uFA), to optimize the accuracy and convergence of the robot and landmarks position estimation. However, it has not shown the performance of the accuracy and convergence. Therefore, this work is done to present both mentioned performances of FastSLAM and uFA-FastSLAM to see which one is better. The result of the experiment shows that uFA-FastSLAM has successfully improved the accuracy (in other words, reduced estimation error) and the convergence consistency of FastSLAM. The proposed uFA-FastSLAM is superior compared to conventional FastSLAM in estimation of landmarks position and robot position with 3.30 percent and 7.83 percent in terms of accuracy model respectively. Furthermore, the proposed uFA-FastSLAM also exhibits better performances compared to FastSLAM in terms of convergence consistency by 93.49 percent and 94.20 percent for estimation of landmarks position and robot position respectively.

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Quantum Circuits for Exact Unitary t-Designs and Applications to Higher-Order Randomized Benchmarking

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PRX Quantum 2021年第3期2卷 030339-030339页

作者： Yoshifumi Nakata Da Zhao Takayuki Okuda Eiichi Bannai Yasunari Suzuki Shiro Tamiya Kentaro Heya Zhiguang Yan Kun Zuo Shuhei Tamate Yutaka Tabuchi Yasunobu Nakamura Photon Science Center Graduate School of Engineering The University of Tokyo Bunkyo-ku Tokyo 113–8656 Japan JST PRESTO 4–1–8 Honcho Kawaguchi Saitama 332–0012 Japan School of Mathematical Sciences Shanghai Jiao Tong University Shanghai 400240 China Department of Mathematics Hiroshima University 1-3-1 Kagamiyama Higashihiroshima 739-8562 Japan Faculty of Mathematics Kyushu University (emeritus) Fukuoka 819-0385 Japan Mathematics Division National Center for Theoretical Sciences National Taiwan University Taipei 10617 Taiwan NTT Computer and Data Science Laboratories NTT Corporation Musashino 180-8585 Japan Department of Applied Physics Graduate School of Engineering The University of Tokyo 7-3-1 Hongo Bynkyo-ku Tokyo 113-8656 Japan Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Meguro-ku Tokyo 153-8904 Japan RIKEN Center for Quantum Computing (RQC) Wako Saitama 351-0198 Japan

A unitary t-design is a powerful tool in quantum information science and fundamental physics. Despite its usefulness, only approximate implementations were known for general t. In this paper, we provide quantum circuits that generate exact unitary t-designs for any t on an arbitrary number of qubits. Our construction is inductive and is of practical use in small systems. We then introduce a tth-order generalization of randomized benchmarking (t-RB) as an application of exact 2t-designs. We particularly study the 2-RB in detail and show that it reveals self-adjointness of quantum noise, a metric related to the feasibility of quantum error correction (QEC). We numerically demonstrate that the 2-RB in one- and two-qubit systems is feasible, and experimentally characterize background noise of a superconducting qubit by the 2-RB. It is shown from the experiment that interactions with adjacent qubits induce the noise that may result in an obstacle toward a realization of QEC.

关键词： Quantum algorithms Quantum benchmarking Quantum gates Quantum protocols

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Location embedding based pairwise distance learning for fine-grained diagnosis of urinary stones

arXiv

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arXiv 2024年

作者： Jin, Qiangguo Huang, Jiapeng Sun, Changming Cui, Hui Xuan, Ping Su, Ran Wei, Leyi Wu, Yu-Jie Wu, Chia-An Duh, Henry B.L. Lu, Yueh-Hsun School of Software Northwestern Polytechnical University Shaanxi China Yangtze River Delta Research Institute of NPU Suzhou China CSIRO Data61 Sydney Australia Department of Computer Science and Information Technology La Trobe University Melbourne Australia School of Engineering Shantou University Guangdong China College of Intelligence and Computing Tianjin University Tianjin China School of Information Xiamen University Xiamen China AIDD Faculty of Applied Science Macao Polytechnic University China Department of Radiology Shuang-Ho Hospital Taipei Medical University Taipei Taiwan Master of Public Health Program National Yang Ming Chiao Tung University Taipei Taiwan School of Design The Hong Kong Polytechnic University Hong Kong Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan

The precise diagnosis of urinary stones is crucial for devising effective treatment strategies. The diagnostic process, however, is often complicated by the low contrast between stones and surrounding tissues, as well as the variability in stone locations across different patients. To address this issue, we propose a novel location embedding based pairwise distance learning network (LEPD-Net) that leverages low-dose abdominal X-ray imaging combined with location information for the fine-grained diagnosis of urinary stones. LEPD-Net enhances the representation of stone-related features through context-aware region enhancement, incorporates critical location knowledge via stone location embedding, and achieves recognition of fine-grained objects with our innovative fine-grained pairwise distance learning. Additionally, we have established an in-house dataset on urinary tract stones to demonstrate the effectiveness of our proposed approach. Comprehensive experiments conducted on this dataset reveal that our framework significantly surpasses existing state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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