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arXiv 2023年

作者： Ahmed, Shams Forruque Alam, Md. Sakib Bin Kabir, Maliha Afrin, Shaila Rafa, Sabiha Jannat Mehjabin, Aanushka Gandomi, Amir H. School of Mathematical Sciences Sunway University Bandar Sunway Petaling Jaya Selangor Darul Ehsan 47500 Malaysia Department of Mathematics & Physics North South University Dhaka1229 Bangladesh Science and Math Program Asian University for Women Chattogram4000 Bangladesh School of Biological Sciences Georgia Institute of Technology AtlantaGA30332 United States Faculty of Engineering & Information Technology University of Technology SydneyNSW2007 Australia Óbuda University Budapest1034 Hungary Department of Computer Science Khazar University Mahsati 41 Baku Azerbaijan

Deep learning (DL) allows computer models to learn, visualize, optimize, refine, and predict data. To understand its present state, examining the most recent advancements and applications of deep learning across various domains is essential. However, prior reviews focused on DL applications in only one or two domains. The current review thoroughly investigates the use of DL in four different broad fields due to the plenty of relevant research literature in these domains. This wide range of coverage provides a comprehensive and interconnected understanding of DL’s influence and opportunities, which is lacking in other reviews. The study also discusses DL frameworks and addresses the benefits and challenges of utilizing DL in each field, which is only occasionally available in other reviews. DL frameworks like TensorFlow and PyTorch make it easy to develop innovative DL applications across diverse domains by providing model development and deployment platforms. This helps bridge theoretical progress and practical implementation. Deep learning solves complex problems and advances technology in many fields, demonstrating its revolutionary potential and adaptability. CNN-LSTM models with attention mechanisms can forecast traffic with 99% accuracy. Fungal-diseased mango leaves can be classified with 97.13% accuracy by the multi-layer CNN model. However, deep learning requires rigorous data collection to analyze and process large amounts of data because it is independent of training data. Thus, large-scale medical, research, healthcare, and environmental data compilation are challenging, reducing deep learning effectiveness. Future research should address data volume, privacy, domain complexity, and data quality issues in DL *** Codes 68T07 © 2023, CC BY.

关键词： Natural language processing systems

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Correlation dimension in empirical networks

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Physical Review E 2023年第3期107卷 034310-034310页

作者： Jack Murdoch Moore Haiying Wang Michael Small Gang Yan Huijie Yang Changgui Gu MOE Key Laboratory of Advanced Micro-Structured Materials and School of Physics Science and Engineering Tongji University Shanghai 200092 People's Republic of China Business School University of Shanghai for Science and Technology Shanghai 200093 People's Republic of China Complex Systems Group Department of Mathematics and Statistics University of Western Australia Crawley 6009 Western Australia Australia Mineral Resources CSIRO Kensington 6151 Western Australia Australia Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai 200092 People's Republic of China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai 200031 People's Republic of China

Network correlation dimension governs the distribution of network distance in terms of a power-law model and profoundly impacts both structural properties and dynamical processes. We develop new maximum likelihood methods which allow us robustly and objectively to identify network correlation dimension and a bounded interval of distances over which the model faithfully represents structure. We also compare the traditional practice of estimating correlation dimension by modeling as a power law the fraction of nodes within a distance to a proposed alternative of modeling as a power law the fraction of nodes at a distance. In addition, we illustrate a likelihood ratio technique for comparing the correlation dimension and small-world descriptions of network structure. Improvements from our innovations are demonstrated on a diverse selection of synthetic and empirical networks. We show that the network correlation dimension model accurately captures empirical network structure over neighborhoods of substantial size and span and outperforms the alternative small-world network scaling model. Our improved methods tend to lead to higher estimates of network correlation dimension, implying that prior studies could have produced or utilized systematic underestimates of dimension.

关键词： Patterns in complex systems Real world networks Data analysis Fractal dimension characterization Information theory

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Theoretical and Experimental Investigation of On-Chip Mid-Infrared Chalcogenide Waveguide Ch4 Sensor Based on Wavelength Modulation Spectroscopy

SSRN

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SSRN 2022年

作者： Pi, Mingquan Huang, Yijun Zhao, Huan Peng, Zihang Lang, Jiaming Ji, Jialin Teng, Ling Song, Fang Liang, Lei Zhang, Yu Zheng, Chuantao Wang, Yiding Tittel, Frank K. State Key Laboratory of Integrated Optoelectronics College of Electronic Science and Engineering Jilin University 2699 Qianjin Street Changchun130012 China School of Mathematics and Statistics Changchun University of Science and Technology Changchun130022 China State Key Laboratory of Luminescence and Applications Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun130033 China Department of Electrical and Computer Engineering Rice University 6100 Main Street HoustonTX77005 United States

Compared with direct absorption spectroscopy (DAS), wavelength modulation spectroscopy (WMS) with good ability of suppressing noise is rarely used in waveguide sensor. The influence of waveguide parameters on WMS sensing performance is required to clarify in both theory and experiment. Three mid-infrared waveguide methane (CH4) sensors (trapezoid, rectangular, suspended) were proposed and optimized for WMS simulation. Theoretical formulation and the experimental sensing noise were used to numerically evaluate the key figure of merit more accurately. Simulation results were presented to show the influence of waveguide parameters on sensing performance. Two chalcogenide on magnesium fluoride (ChG-on-MgF2) trapezoid waveguide sensors were fabricated using lift-off method, and CH4 measurement was carried out using WMS. The waveguide loss was measured to be 1.57 dB/cm, leading to an optimal sensor length of 2.7 cm. The limit of detection (LoD) for the 1cm- and 2cm-long waveguide sensors are 0.68% and 0.17% with an averaging time of 0.2 s. The experimentally achieved second harmonic (2f) signal amplitude and LoD show good agreement with the theoretical results, verifying the feasibility of the design and analysis model of the WMS-based waveguide sensor. Due to the use of WMS and a reduction of waveguide loss from 3.6 to 1.57 dB/cm, the LoD of the 2cm-long ChG-on-MgF2 sensor is 24 times lower than our previously reported 2cm-long ChG-on-SiO2 sensor based on DAS. This work provides a systematic guidance for the design of waveguide gas sensor based on WMS and contributes to improving the sensitivity of on-chip gas sensing. © 2022, The Authors. All rights reserved.

关键词： Chalcogenides

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Breast cancer detection based on PCA feature selection process using an ensemble learning algorithm

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AIP Conference Proceedings 2024年第1期2937卷

作者： Praveen Kumar Misra G. Meena Devi Yoni Magali Maita Cruz Juan Carlos Cotrina-Aliaga Pratik Chatterjee Durgaprasad Gangodkar Department of Mathematics and Statistics Dr. Shakuntala Misra National Rehabilitation University Lucknow 226017 Uttar Pradesh India. Department of Mathematics St. Joseph’s college of Engineering Chennai 600119 India. Cesar Vallejo University Peru Brazil. Daniel Alcides Carrion Institute Peru Brazil. Department of Biotechnology Vellore Institute of Technology Vellore 632014 Tamilnadu India. Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand 248002 India.

Breast Cancer (BC), more than any other malignancy, is one of the main reasons of mortality amongst women. Due to disease’s intricacy, varying patient population samples and changing treatment protocols, accurate diagnosis of breast cancer is extremely challenging. Better diagnostic procedures are critical for tailored care and treatment, as well as reducing and controlling cancer recurrence. This study investigates the early identification of breast cancer using a variety of ensemble methodologies. The dataset that has been utilized in this work is chosen to forecast breast cancer based on nine individual variables, including BMI, age, insulin, glucose and a homeostasis model evaluation. This research provides a suggested strategy based on feature selection that uses Principal Component Analysis (PCA) as well as the ensemble technique for dramatically lessening the dimensionality of features and enhance breast cancer classification. The suggested method is tested using the Wisconsin Breast Cancer Dataset, which is open to the public (WBCD dataset). This study focuses on evaluating and comparing the efficacy of several ensemble approaches based on PCA feature selection in early diagnosis of BC. LogitBoost is the most precise model amongst all the ensemble approaches, according to the findings. The LogitBoost algorithm beats the other ensemble methods, with an accuracy of 72.46 percent. The findings of data analysis show that ensemble classifiers outperform state-of-the-art approaches in detecting BC.

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Text data labeling process for semi-supervised learning modeling

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AIP Conference Proceedings 2024年第1期3165卷

作者： Ahmad Abdul Chamid Widowati Widowati Retno Kusumaningrum Doctoral Program in Information Systems School of Postgraduate Studies Diponegoro University Jl. Prof Sudarto S.H. Tembalang Semarang 50275 Indonesia Department of Informatics Engineering Faculty of Engineering Muria Kudus University Jl. Lingkar Utara UMK Gondangmanis Kudus 59327 Indonesia Department of Mathematics Faculty of Science and Mathematics Diponegoro University Jl. Prof Sudarto S.H. Tembalang Semarang 50275 Indonesia Department of Informatics Faculty of Science and Mathematics Diponegoro University Jl. Prof Sudarto S.H. Tembalang Semarang 50275 Indonesia

To develop a semi-supervised learning model, it is necessary to have a little labeled data and a lot of unlabeled data. The data labeling process requires great effort and must involve experts. In the process of labeling text data requires a linguist to do the labeling. In this study using text data in the form of marketplace user reviews, in general the data is still dirty and data preprocessing needs to be done. The data preprocessing techniques applied are normalization, tokenization, stopword removal, and stemming. After the data goes through the preprocessing stage, labeling is carried out by involving more than one linguist. The results of the labeling that have been carried out by linguists will be carried out a consistency test process by applying the Cohen Kappa method. The data that has been labeled by 2 linguists is 820 data, after the consistency test is obtained the delivery aspect with a kappa value of 1, the service aspect with a kappa value of 0.960, the quality aspect with a kappa value of 0.981, the size aspect with a kappa 0.997, the color aspect with a kappa value of 1. Based on the results of the consistency test from the 2 experts for the 5 aspects, consistent results were obtained. So that the data that has been labeled can be used as training data to carry out the process of developing a semi-supervised learning model. A series of labeling processes up to consistency testing will be the main basis for developing a semi-supervised learning model.

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Inertial proximal ADMM for separable multi-block convex optimizations and compressive affine phase retrieval

arXiv

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arXiv 2020年

作者： Li, Peng Chen, Wengu Sun, Qiyu School of Mathematics and Statistics Lanzhou University LanzhouGansu China Graduate School China Academy of Engineering Physics Beijing100088 China Institute of Applied Physics and Computational Mathematics Beijing100088 China Department of Mathematics University of Central Florida OrlandoFL32816 United States

Separable multi-block convex optimization problem appears in many mathematical and engineering fields. In the first part of this paper, we propose an inertial proximal ADMM to solve a linearly constrained separable multi-block convex optimization problem, and we show that the proposed inertial proximal ADMM has global convergence under mild assumptions on the regularization matrices. Affine phase retrieval arises in holography, data separation and phaseless sampling, and it is also considered as a nonhomogeneous version of phase retrieval that has received considerable attention in recent years. Inspired by convex relaxation of vector sparsity and matrix rank in compressive sensing and by phase lifting in phase retrieval, in the second part of this paper, we introduce a compressive affine phase retrieval via lifting approach to connect affine phase retrieval with multi-block convex optimization, and then based on the proposed inertial proximal ADMM for multi-block convex optimization, we propose an algorithm to recover sparse real signals from their (noisy) affine quadratic measurements. Our numerical simulations show that the proposed algorithm has satisfactory performance for affine phase retrieval of sparse real *** Codes 65K10, 94A12, 42C15, 52A41 Copyright © 2020, The Authors. All rights reserved.

关键词： Convex optimization

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TAROGE-M: Antarctic High-Mountain Radio Antenna Array for Detecting Ultra-High Energy ANITA Anomalous Events 38

TAROGE-M: Antarctic High-Mountain Radio Antenna Array for De...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Wang, Shih-Hao Chen, Pisin Chen, Yaocheng Chen, Ying-Chih Choi, Taejin Ham, Young-Bae Hsu, Shih-Ying Huang, Jian-Jung Huang, MingHuey A. Jee, Geonhwa Jung, Jongil Kim, Jieun Kuo, Chung-Yun Kwon, Hyuck-Jin Lee, Changsup Leung, Chung-Hei Liu, Tsung-Che Nam, Jiwoo Shiao, Yu-Shao J. Shin, Bok-Kyun Su, Shih-Chieh Wang, Min-Zu Wang, Shih-Hao Wang, Yu-Hsin Anker, Astrid Barwick, Steven W. Besson, Dave Z. Bouma, Sjoerd Cataldo, Maddalena Gaswint, Geoffrey Glaser, Christian Hallmann, Steffen Hanson, Jordan C. Henrichs, Jakob Kleinfelder, Stuart A. Lahmann, Robert Meyers, Zachary S. Nelles, Anna Novikov, Alexander Paul, Manuel P. Pyras, Lilly Persichilli, Christopher Plaisier, Ilse Rice-Smith, Ryan Seikh, Mohammad F. H. Tatar, Joulien Welling, Christoph Zhao, Leshan Department of Physics National Taiwan University Taipei10617 Taiwan Leung Center for Cosmology and Particle Astrophysics National Taiwan University Taipei10617 Taiwan Kavli Institute for Particle Astrophysics and Cosmology SLAC National Accelerator Laboratory Stanford University StanfordCA94305 United States Division of Atmospheric Sciences Korea Polar Research Institute Incheon Korea Republic of Department of Polar Science Korea University of Science and Technology Daejeon Korea Republic of Department of Energy Engineering National United University Miaoli Taiwan Department of Astronomy Space Science and Geology Chungnam National University Daejeon34134 Korea Republic of Department of Physics and Astronomy University of Delaware NewarkDE19716 United States Department of Electrophysics National Yang Ming Chiao Tung University Hsinchu300 Taiwan Department of applied physics National Pingtung university Pingtung900 Taiwan Chung Cheng Institute of Technology National Defense University Taoyuan335 Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu300 Taiwan National Nano Device Laboratories Hsinchu300 Taiwan Ulsan National Institute of Science and Technology Ulsan44919 Korea Republic of Institute of Astronomy and Astrophysics Academia Sinica Astronomy-Mathematics Building No. 1 Sec. 4 Roosevelt Rd. Taipei10617 Taiwan Department of Physics and Astronomy University of California IrvineCA92697 United States Department of Physics and Astronomy University of Kansas LawrenceKS66045 United States DESY Zeuthen15738 Germany ECAP Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen91058 Germany Uppsala University Department of Physics and Astronomy UppsalaSE-752 37 Sweden Whittier College Department of Physics WhittierCA90602 United States Department of Electrical Engineering and Computer Science University of California IrvineCA92697 United States Resea

TAROGE-M is a radio antenna array atop ∼ 2.7 km-high Mt. Melbourne in Antarctica for detecting ultra-high energy (UHE, E > 1017 eV) air showers in near-horizontal directions. Besides the detection of cosmic rays and Earth-skimming tau neutrinos, its primary goal is to reproduce the discovery and verify the origin of so-called ANITA anomalous events, having feature of upward-going UHE air showers but cannot be explained by tau neutrinos. The detection concept takes advantages of a high altitude for a broad view toward the horizon;strong and near-vertical geomagnetic field for enhancing the radio signal;and quiet radio environment in Antarctica. Its relatively simple design and high duty cycle make it easily to be extended and thus to achieve an exposure competitive with ANITA experiments within a few years. The first TAROGE-M station operating at 180-450 MHz frequencies deployed in 2020 detected seven UHE cosmic ray events within 25.3-day livetime. The events have a mean energy of ∼ 1 EeV and an estimated flux consistent with other experiments, and validate the station as an UHE particle detector. In 2022-2023 season, the system was upgraded for a robust long-term operation, with a more detailed calibration with a drone-borne pulser. We also discuss the planned upgrade, including the implementation of interferometric trigger, which is expected to increase the cosmic ray and tau neutrino acceptances by a factor of 10 at 0.1 EeV, corresponding to a lower energy threshold by a factor of 3. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmic rays

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Transition between boundary-limited and mixing-length scalings of turbulent transport in internally heated convection

arXiv

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arXiv 2021年

作者： Kazemi, Sina Goluskin, David Ostilla-Mónico, Rodolfo Department of Mechanical Engineering University of Houston HoustonTX77040 United States Department of Mathematics Statistics University of Victoria VictoriaBCV8P 5C2 Canada Escuela Superior de Ingenería Universidad de Cádiz Cádiz Spain

Heat transport in turbulent thermal convection increases with the thermal forcing, but in almost all studies the rate of this increase is slower than it would be if transport became independent of the molecular diffusivities-the heat transport scaling exponent is smaller than the mixing-length (or 'ultimate') value of 1∕2. This is due to thermal boundary layers that throttle heat transport in configurations driven either by thermal boundary conditions or by internal heating, giving a scaling exponent close to the boundary-limited (or 'classical) value of 1∕3. With net-zero internal heating and cooling in different regions, the larger mixing-length exponent can be attained because heat need not cross a boundary. We report numerical simulations in which heating and cooling are unequal. As heating and cooling rates are made closer, the scaling exponent of heat transport varies from its boundary-limited value to its mixing-length value. Copyright © 2021, The Authors. All rights reserved.

关键词： Mixing

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Retraction Notice to “Implementation of modified Buongiorno's Model for the investigation of chemically reacting rGO- Fe 3 O 4 -TiO 2 -H 2 O Ternary nanofluid jet flow in the presence of bio-active mixers” [Chemical Physics Letters 786 (2022) 139194]

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Chemical Physics Letters 2025年 865卷

作者： V. Puneeth Rajeev Anandika S. Manjunatha Muhammad Ijaz Khan M. Imran Khan Ali Althobaiti Ahmed M. Galal Department of Mathematics CHRIST(Deemed to be University) Bangalore 560029 India Department of Sciences and Humanities CHRIST(Deemed to be University) Bangalore 560074 India Department of Mathematics and Statistics Riphah International University I-14 Islamabad 44000 Pakistan Department of Mechanics and Engineering Science Peking University Beijing 100871 People's Republic of China Division of Sustainable Development College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar Department of Mathematics College of Science Taif University P·O. Box 11099 Taif 21944 Saudi Arabia Mechanical Engineering Department College of Engineering Prince Sattam Bin Abdulaziz University Wadi addawaser 11991 Saudi Arabia Production Engineering and Mechanical Design Department Faculty of Engineering Mansoura University P.O 35516 Mansoura Egypt

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Tissue oxygenation changes in response to radiation therapy in breast cancer subjects using near-infrared optical imaging

Tissue oxygenation changes in response to radiation therapy ...

引用

Clinical and Translational Biophotonics, Translational 2020

作者： Robledo, Edwin Leiva, Kevin Beiner, Corina Murillo, Juan Rodrigues, Maria Amelia Chuong, Michael Wu, Wensong Godavarty, Anuradha Optical Imaging Laboratory Department of Biomedical Engineering Florida International University MiamiFL33174 United States Department of Radiation Oncology Miami Cancer Institute Baptist Health South Florida 8900 N Kendall Dr MiamiFL33176 United States Department of Mathematics and Statistics Florida International University MiamiFL33174 United States

Tissue oxygenation changes in response to radiation therapy in breast cancer subjects, with decreased oxygen saturation due to onset of radiation dermatitis, as observed via near-infrared optical imaging studies. ... 详细信息

ISBN: (纸本)9781943580743

关键词： Radiotherapy

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