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Decision based algorithm for circular pythagorean fuzzy framework and advanced petroleum exploration methods

Scientific reports 2025年第1期15卷 19212页

作者： Maria Nazir Zeeshan Ali Abrar Hussain Kifayat Ullah Oumaima Saidani Department of Mathematics Riphah International University Lahore (Lahore Campus Lahore 54000 Pakistan. Department of Information Management National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin 64002 Taiwan R.O.C. Department of Mathematics Riphah International University Lahore (Lahore Campus Lahore 54000 Pakistan. abrar353eb@***. Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai 602105 India. Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia. ocsaidani@pnu.edu.sa.

This presentation articulates different terminologies of fuzzy mathematical approaches and decision-making techniques for handling vague-type human opinions. To achieve this purpose, we explore a novel approach of circular pythagorean fuzzy set (Cr-PyFS), which is an extended framework of intuitionistic and pythagorean fuzzy sets. A Cr-PyFS expresses expert opinion with an additional term radius of a circle among an element's membership grade and non-membership grade. Moreover, we also illustrate the theory of Hamy mean (HM) models to define correlation among different input arguments and preferences. By combining theories of HM models and Cr-PyFSs, we derived new mathematical methodologies, including circular pythagorean fuzzy HM (Cr-PyFHM), circular pythagorean fuzzy weighted HM (Cr-PyFWHM), circular pythagorean fuzzy Dual HM (Cr-PyFDHM) and circular pythagorean fuzzy weighted Dual HM (Cr-PyFDHM) operators. All aggregation operators (AOs) are verified through different mathematical properties and special cases. An intelligent decision algorithm of the multi-attribute decision-making (MADM) problem is modified considering circular pythagorean fuzzy information. To show the reliability and effectiveness of developed approaches, we discussed an application related to the exploration and production of the petroleum industry. An experimental case study is resolved using derived mathematical approaches and decision-making terminologies. The setting of different parametric variables in sensitivity analysis verifies the advantages and reliability of designed approaches. A comparison approach is conducted to compare the results of pioneered approaches with existing AOs.

关键词： Circular pythagorean fuzzy information Hamy mean models Petroleum exploration methods and decision support systems

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Corrigendum to "Novel dynamic fuzzy Decision-Making framework for COVID-19 vaccine dose recipients" [J. Adv. Res. 37 (2022) 147-168]

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Journal of advanced research 2023年 45卷 193页

作者： O S Albahri A A Zaidan A S Albahri H A Alsattar Rawia Mohammed Uwe Aickelin Gang Kou F M Jumaah Mahmood M Salih A H Alamoodi B B Zaidan Mamoun Alazab Alhamzah Alnoor Jameel R Al-Obaidie Computer Techniques Engineering Department Mazaya University College Thi-Qar Nassiriya Iraq. Faculty of Engineering & IT British University in Dubai United Arab Emirates. Electronic address: aws.alaa@fskik.upsi.edu.my. Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq. Faculty of Engineering & IT British University in Dubai United Arab Emirates. School of Computing and Information Systems University of Melbourne 700 Swanston Street Victoria 3010 Australia. School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Chengdu 611130 China. Department of Advanced Applications and Embedded Systems Intel Corporation Plot 6 Bayan Lepas Technoplex 11900 Georgetown Pulau Pinang Malaysia. Department of Computer Science Computer Science and Mathematics College Tikrit University Tikrit Iraq. Department of Computing Faculty of Arts Computing and Creative Industry Universiti Pendidikan Sultan Idris (UPSI) Perak Malaysia. Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin 64002 Taiwan ROC. College of Engineering IT and Environment Charles Darwin University NT Australia. School of Management Universiti Sains Malaysia Pulau Pinang Malaysia. Department of Biology Faculty of Science and Mathematics Universiti Pendidikan Sultan Idris Tanjong Malim Perak 35900 Malaysia.

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Fully dense neural network for the automatic modulation recognition

arXiv

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arXiv 2019年

作者： Du, Miao Yu, Qin Fei, Shaomin Wang, Chen Gong, Xiaofeng Luo, Ruisen College of Electrical Engineering Sichuan University 24 South Section 1 One Ring Road Chengdu610065 China Department of Computer Science Rutgers University - New Brunswick PiscatawayNJ08854 United States Sichuan University Shaomin China Engineering Practice Center Chengdu University of Information Technology

Nowadays, we mainly use various convolution neural network (CNN) structures to extract features from radio data or spectrogram in AMR. Based on expert experience and spectrograms, they not only increase the difficulty of preprocessing, but also consume a lot of memory. In order to directly use in-phase and quadrature (IQ) data obtained by the receiver and enhance the efficiency of network extraction features to improve the recognition rate of modulation mode, this paper proposes a new network structure called Fully Dense Neural Network (FDNN). This network uses residual blocks to extract features, dense connect to reduce model size, and adds attentions mechanism to recalibrate. Experiments on RML2016.10a show that this network has a higher recognition rate and lower model complexity. And it shows that the FDNN model with dense connections can not only extract features effectively but also greatly reduce model parameters, which also provides a significant contribution for the application of deep learning to the intelligent radio system. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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Reading race: AI recognizes patient's racial identity in medical images

arXiv

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arXiv 2021年

作者： Banerjee, I. Bhimireddy, A.R. Burns, J.L. Celi, L.A. Chen, L. Correa, R. Dullerud, N. Ghassemi, M. Gichoya, J.W. Huang, S. Kuo, P. Lungren, M.P. Price, B.J. Purkayastha, S. Pyrros, A.A. Oakden-Rayner, L. Okechukwu, C. Seyyed-Kalantari, L. Trivedi, H. Wang, R. Zaiman, Z. Zhang, H. Department of Biomedical Informatics Emory University 100 Woodruff Circle AtlantaGA30322 United States School of Informatics and Computing Indiana University Purdue University 535 W Michigan St. IT 475E IndianapolisIN46202 United States Institute for Medical Engineering and Science Massachusetts Institute of Technology 45 Carleton Street E25- 330 CambridgeMA02142 United States Department of Medicine Beth Israel Deaconess Medical Center 330 Brookline Avenue BostonMA02215 United States Department of Computer Science National Tsing Hua University No. 101 Section 2 Kuang-Fu Road Hsinchu30013 Taiwan Department of Computer Science Emory University 201 Dowman Drive AtlantaGA30322 United States Department of Computer Science University of Toronto 214 College St TorontoONM5T 3A1 Canada Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Department of Radiology Emory University 1364 Clifton Rd AtlantaGA30322 United States Stanford University School of Medicine 291 Campus Drive Palo AltoCA94305 United States Florida State University College of Medicine 1115 W Call St TallahasseeFL32304 United States Australian Institute for Machine Learning University of Adelaide AIML Building Lot Fourteen Cnr North Terrace and Frome Road AdelaideSA5000 Australia Department of Computer Science Georgia Institute of Technology 266 Ferst Drive AtlantaGA30332 United States Lunenfeld-Tanenbaum Research Institute Sinai Health 600 University Ave TorontoONM5G 1X5 Canada Dupage Medical Group 40 S Clay St HinsdaleIL60521 United States

Background: In medical imaging, prior studies have demonstrated disparate AI performance by race, yet there is no known correlation for race on medical imaging that would be obvious to the human expert interpreting the images. We conduct a comprehensive evaluation of AI's ability to recognize patient's racial identity from medical images. Methods: Using private and public datasets we evaluate: A) performance quantification of deep learning models to detect race from medical images, including the ability of these models to generalize to external environments and across multiple imaging modalities, B) assessment of possible confounding anatomic and phenotype population features, such as disease distribution and body habitus as predictors of race, and C) investigation into the underlying mechanism by which AI models can recognize race. Findings: Standard deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities. Our findings hold under external validation conditions, as well as when models are optimized to perform clinically motivated tasks. We demonstrate this detection is not due to trivial proxies or imaging-related surrogate covariates for race, such as underlying disease distribution. Finally, we show that performance persists over all anatomical regions and frequency spectrum of the images suggesting that mitigation efforts will be challenging and demand further study. Interpretation: We emphasize that model ability to predict self-reported race is itself not the issue of importance. However, our findings that AI can trivially predict self-reported race - even from corrupted, cropped, and noised medical images - in a setting where clinical experts cannot, creates an enormous risk for all model deployments in medical imaging: if an AI model secretly used its knowledge of self-reported race to misclassify all Black patients, radiologists would not be able to tell using the same data the model ha

关键词： Medical imaging

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Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition

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Database : the journal of biological databases and curation 2023年第2023期2023卷 baac108页

作者： Davy Weissenbacher Karen O'Connor Siddharth Rawal Yu Zhang Richard Tzong-Han Tsai Timothy Miller Dongfang Xu Carol Anderson Bo Liu Qing Han Jinfeng Zhang Igor Kulev Berkay Köprü Raul Rodriguez-Esteban Elif Ozkirimli Ammer Ayach Roland Roller Stephen Piccolo Peijin Han V G Vinod Vydiswaran Ramya Tekumalla Juan M Banda Parsa Bagherzadeh Sabine Bergler João F Silva Tiago Almeida Paloma Martinez Renzo Rivera-Zavala Chen-Kai Wang Hong-Jie Dai Luis Alberto Robles Hernandez Graciela Gonzalez-Hernandez Department of Computational Biomedicine Cedars-Sinai Medical Center Los Angeles CA USA. DBEI The Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd Zhongli District Taoyuan 320 Taiwan. IoX Center National Taiwan University Da'an District Section 4 Roosevelt Rd No. 1 Barry Lam Hall Taipei 106 Taiwan. Research Center for Humanities and Social Sciences Academia Sinica No. 128 Section 2 Academia Rd Nangang District Taipei 115 Taiwan. Computational Health Informatics Program Boston Children's Hospital Boston MA USA. Department of Pediatrics Harvard Medical School Boston MA USA. NVIDIA Santa Clara CA USA. Department of Statistics Florida State University Tallahassee FL USA. Data and Analytics Chapter F. Hoffmann-La Roche Ltd Switzerland. Pharmaceutical Research and Early Development Roche Innovation Center Basel Switzerland. Speech and Language Technology Lab DFKI Berlin Germany. Department of Biology Brigham Young University Provo UT USA. Department of Computational Medicine and Bioinformatics Medical School University of Michigan Ann Arbor MI USA. Department of Learning Health Sciences Medical School University of Michigan Ann Arbor MI USA. School of Information University of Michigan Ann Arbor MI USA. Department of Computer Science Georgia State University Atlanta GA USA. CLaC Labs Concordia University Montreal Canada. DETI Institute of Electronics and Informatics Engineering of Aveiro University of Aveiro Portugal. Department of Computation University of A Coruña Spain. Computer Science and Engineering Department Universidad Carlos III de Madrid Madrid Spain. Big Data Laboratory Chunghwa Telecom Laboratories Taoyuan Taiwan. Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan. Department of Electrical Engineering College of Electrical Engineering and Computer Sci

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user's timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user's timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://***/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.

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Devanagari Handwriting Grading System Based on Curvature Features

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computer Modeling in engineering & Sciences 2017年第2期113卷 195-202页

作者： Munish Kumar Simpel Rani Jindal Department of Computer Applications GZS Campus College of Engineering&Technology(Maharaja Ranjit Singh Punjab Technical University)BathindaPunjabIndia Computer Science and Engineering Section Yadavindera College of EngineeringTalwandi SaboBathindaPunjabIndia.

Grading of writers in perspective of their handwriting is a challenging task owing to various writing styles of different *** paper presents a framework for grading of Devanagari writers in perspective of their *** framework of grading can be useful in conducting the handwriting competitions and then deciding the winners on the basis of an automated *** the set of features is a challenging task for implementing a handwriting grading system of particular *** this paper,curvature features,namely,parabola curve fitting and power curve fitting have been considered for extracting the vital information of writers,which can be helpful in grading the *** obtaining the classification score,k-NN classifier has been considered in the present *** printed Devanagari font styles,namely,Devlys,Krishna,Krutidev,and Utsaah have been considered for train the proposed model of handwriting *** evaluating the effectiveness of the proposed framework,we have conducted a mock test of 75 Devanagari writers(Left handed and Right handed)and noticed that the proposed framework performing well suitable for conducting the handwriting competition of Devanagari text *** work is also newly in the ground of Devanagari text recognition.

关键词： Handwritten character recognition curvature features parabola curve fitting power curve fitting k-NN

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Adaptive and Personalized Plasma Insulin Concentration Estimation for Artificial Pancreas Systems

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Journal of Diabetes Science and technology 2018年第3期12卷 639-649页

作者： Hajizadeh, Iman Rashid, Mudassir Samadi, Sediqeh Feng, Jianyuan Sevil, Mert Hobbs, Nicole Lazaro, Caterina Maloney, Zacharie Brandt, Rachel Yu, Xia Turksoy, Kamuran Littlejohn, Elizabeth Cengiz, Eda Cinar, Ali Department of Chemical and Biological Engineering Illinois Institute of Technology Chicago IL United States Department of Biomedical Engineering Illinois Institute of Technology Chicago IL United States Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago IL United States School of Information Science and Technology Northeastern University Shenyang China Department of Pediatrics and Medicine Section of Endocrinology Kovler Diabetes Center University of Chicago Chicago IL United States Department of Pediatrics Yale University School of Medicine New Haven CT United States

Background: The artificial pancreas (AP) system, a technology that automatically administers exogenous insulin in people with type 1 diabetes mellitus (T1DM) to regulate their blood glucose concentrations, necessitates the estimation of the amount of active insulin already present in the body to avoid overdosing. Method: An adaptive and personalized plasma insulin concentration (PIC) estimator is designed in this work to accurately quantify the insulin present in the bloodstream. The proposed PIC estimation approach incorporates Hovorka’s glucose-insulin model with the unscented Kalman filtering algorithm. Methods for the personalized initialization of the time-varying model parameters to individual patients for improved estimator convergence are developed. Data from 20 three-days-long closed-loop clinical experiments conducted involving subjects with T1DM are used to evaluate the proposed PIC estimation approach. Results: The proposed methods are applied to the clinical data containing significant disturbances, such as unannounced meals and exercise, and the results demonstrate the accurate real-time estimation of the PIC with the root mean square error of 7.15 and 9.25 mU/L for the optimization-based fitted parameters and partial least squares regression-based testing parameters, respectively. Conclusions: The accurate real-time estimation of PIC will benefit the AP systems by preventing overdelivery of insulin when significant insulin is present in the bloodstream. © 2018, © 2018 Diabetes technology Society.

关键词： artificial pancreas glucose control hypoglycemia mitigation insulin on board plasma insulin concentration

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Guidance Vector Field Encoding based on Contraction Analysis 16

Guidance Vector Field Encoding based on Contraction Analysis

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16th European Control Conference, ECC 2018

作者： Wu, Chengshuai Chen, Jian Jeltsema, DImitri Dai, And Chenxi State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China Section Control Systems Engineering HAN University of Applied Science P.O. Box 2217 Arnhem6802 CE Netherlands Department of Electrical and Computer Engineering Ohio State University Columbus43210 United States

ISBN: (纸本)9783952426982

The guidance vector field encoding problem is revisited via exploiting the recently developed differential Lyapunov framework for contraction analysis. In control applications, a designed guidance vector field is utilized to guide a particle (such as a robot, or an end actuator) to converge to and circulate along a target curve, such as robot navigation, or motion planing. This paper proposes some new methods for guidance vector filed encoding. The methods analyze the guidance vector field encoding problem from a theoretical perspective providing insights into the problem, and it shows that the contraction analysis is a natural theoretical tool for guidance vector encoding. Furthermore, the differences and relations between the proposed result and the previous methods are discussed. Simulation examples are presented to illustrate the effectiveness of the proposed methods. © 2018 European Control Association (EUCA).

关键词： Robots

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A distributed gossip optimization algorithm for wireless multi-hop networks 4th

A distributed gossip optimization algorithm for wireless mul...

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4th iCatse Conference on Mobile and Wireless technology, ICMWT 2017

作者： Liu, Jain-Shing Chang, Wan-Ling Department of Computer Science and Information Engineering Providence University 200 Section 7 Taiwan Boulevard Shalu District Taichung City43301 Taiwan Department of Insurance Chaoyang University of Technology 168 Jifeng East Road Wufeng District Taichung41349 Taiwan

ISBN: (纸本)9789811052804

In this paper, we study a constrained network lifetime maximization problem in wireless sensor networks, and introduce a cross-layer formulation with general NUM (network utility maximization) that accommodates routing, scheduling and stream control from different layers of network. Specifically, for this problem, we derive a gossip-based formulation for the consensus agreement on the variables involved, and develop an asynchronous decentralized algorithm specific to the optimization problem. Our numerical experiments exhibit its results, showing that the gossip-based consensus algorithm can actually achieve the optimization objective by means of the simple and robust asynchronous operations developed. © Springer Science+Business Media Singapore 2018.

关键词： Wireless sensor networks

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BenthicNet: A global compilation of seafloor images for deep learning applications

arXiv

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arXiv 2024年

作者： Lowe, Scott C. Misiuk, Benjamin Xu, Isaac Abdulazizov, Shakhboz Baroi, Amit R. Bastos, Alex C. Best, Merlin Ferrini, Vicki Friedman, Ariell Hart, Deborah Hoegh-Guldberg, Ove Ierodiaconou, Daniel Mackin-McLaughlin, Julia Markey, Kathryn Menandro, Pedro S. Monk, Jacquomo Nemani, Shreya O’Brien, John Oh, Elizabeth Reshitnyk, Luba Y. Robert, Katleen Roelfsema, Chris M. Sameoto, Jessica A. Schimel, Alexandre C.G. Thomson, Jordan A. Wilson, Brittany R. Wong, Melisa C. Brown, Craig J. Trappenberg, Thomas Vector Institute TorontoON Canada Memorial University of Newfoundland Department of Geography St. John’sNL Canada Memorial University of Newfoundland Department of Earth Sciences St. John’sNL Canada Dalhousie University Faculty of Computer Science HalifaxNS Canada Dalhousie University School for Resource and Environmental Studies HalifaxNS Canada Dalhousie University DeepSense HalifaxNS Canada Universidade Federal do Espírito Santo Departamento de Oceanografia e Ecologia Vitória Brazil Fisheries and Oceans Canada Marine Spatial Ecology and Analysis Section Institute of Ocean Sciences SidneyBC Canada Columbia University Lamont-Doherty Earth Observatory PalisadesNY United States University of Sydney Australian Centre for Field Robotics SydneyNSW Australia Greybits Engineering SydneyNSW Australia National Oceanic and Atmospheric Administration Northeast Fisheries Science Center Woods HoleMA United States University of Queensland School of the Environment BrisbaneQLD Australia Deakin University School of Life and Environmental Sciences WarrnamboolVIC Australia Oxy Occidental College Vantuna Research Group Los AngelesCA United States University of Tasmania Institute for Marine and Antarctic Studies HobartTAS Australia Fisheries and Marine Institute of Memorial University of Newfoundland School of Ocean Technology St. John’sNL Canada Fisheries and Oceans Canada Bedford Institute of Oceanography DartmouthNS Canada Hakai Institute Heriot Bay BC Canada Trondheim Norway Ecology Action Centre HalifaxNS Canada Dalhousie University Department of Oceanography HalifaxNS Canada

Advances in underwater imaging enable collection of extensive seafloor image datasets necessary for monitoring important benthic ecosystems. The ability to collect seafloor imagery has outpaced our capacity to analyze it, hindering mobilization of this crucial environmental information. Machine learning approaches provide opportunities to increase the efficiency with which seafloor imagery is analyzed, yet large and consistent datasets to support development of such approaches are scarce. Here we present BenthicNet: a global compilation of seafloor imagery designed to support the training and evaluation of large-scale image recognition models. An initial set of over 11.4 million images was collected and curated to represent a diversity of seafloor environments using a representative subset of 1.3 million images. These are accompanied by 3.1 million annotations translated to the CATAMI scheme, which span 190 000 of the images. A large deep learning model was trained on this compilation and preliminary results suggest it has utility for automating large and small-scale image analysis tasks. The compilation and model are made openly available for reuse at doi:10.20383/103.0614. © 2024, CC BY.

关键词： Underwater imaging

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