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Transportation engineering 2023年 14卷

作者： Dasoomi, MohammadHanif Naderan, Ali Allahviranloo, Tofigh PhD. Candidate Department of Civil Engineering Science and Research Branch Islamic Azad University Tehran14778-93855 Iran Assistant Professor Department of Civil Engineering Science and Research Branch Islamic Azad University Tehran14778-93855 Iran Professor Department of Mathematical Sciences Science and Research Branch Islamic Azad University Tehran14778-93855 Iran Professor Faculty of Engineering and Natural Sciences Istinye University Istanbul Turkey

Online and offline shopping trips have a profound impact on various facets of urban life, including e-commerce, transportation systems, and sustainability. To assess the factors shaping consumers' decisions, we introduce a novel hybrid machine learning model that integrates the Gray Wolf Optimization (GWO) algorithm with a deep Convolutional Neural Network (CNN). This model is applied to predict shopping behavior based on a survey of 1000 active e-commerce users residing in areas 2 and 5 of Tehran. These individuals have made successful purchases through both online and offline services during the final 20 days of 2021. The GWO algorithm plays a pivotal role in selecting optimal features and hyperparameters for the deep Convolutional Neural Network, which is a powerful deep learning model for image recognition and classification. Notably, our model achieves an impressive accuracy of 97.81% while maintaining a MSE of 0.325, having identified seven out of ten key features as the most influential. To gage the effectiveness of our approach, we conduct a comparative analysis with alternative methods. The results unequivocally showcase the superiority of our proposed algorithm, which attains an accuracy of 97.81%. In contrast, other models such as CNN, LSTM, MLP, DT, and KNN yield accuracies of 95.63%, 94.04%, 90.12%, 86.49%, and 80.16%, respectively. This study offers valuable insights for transportation planners, e-commerce managers, and policymakers. Its primary objective is to assist them in formulating effective strategies aimed at reducing transportation costs, curbing pollutant emissions, mitigating urban traffic congestion, and enhancing user satisfaction all while fostering sustainable development. © 2023 The Author(s)

关键词： Neural network models

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Supervised Machine-Learning Algorithms in Real-time Prediction of Hypotensive Events*

Supervised Machine-Learning Algorithms in Real-time Predicti...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Mina Chookhachizadeh Moghadam Ehsan Masoumi Nader Bagherzadeh Davinder Ramsingh Zeev N. Kain PhD Candidate of Computer Engineering University of California Irvine CA USA Research Collaborator with the Department of Electrical Engineering and computer science University of California Irvine CA USA Faculty of Electrical Engineering and Computer Science University of California Irvine CA USA Faculty of School of Medicine Loma Linda University Medical Center Loma Linda CA USA Chancellor’s Professor and Executive Director University of California Irvine CA USA

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Hypotension is common in critically ill patients. Early prediction of hypotensive events in the Intensive Care Units (ICUs) allows clinicians to pre-emptively treat the patient and avoid possible organ damage. In this study, we investigate the performance of various supervised machine-learning classification algorithms along with a real-time labeling technique to predict acute hypotensive events in the ICU. It is shown that logistic regression and SVM yield a better combination of specificity, sensitivity and positive predictive value (PPV). Logistic regression is able to predict 85% of events within 30 minutes of their onset with 81% PPV and 96% specificity, while SVM results in 96% specificity, 83% sensitivity and 82% PPV. To further reduce the false alarm rate, we propose a high-level decision-making algorithm that filters isolated false positives identified by the machine-learning algorithms. By implementing this technique, 24% of the false alarms are filtered. This saves 21 hours of medical staff time through 2,560 hours of monitoring and significantly reduces the disturbance caused by alarming monitors.

关键词： Prediction algorithms Sensitivity Support vector machines Decision making Labeling Logistics Monitoring

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Changing life expectancy in European countries 1990–2021: a subanalysis of causes and risk factors from the Global Burden of Disease Study 2021

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The Lancet Public Health 2025年第3期10卷 e172-e188页

作者： Steel, Nicholas Bauer-Staeb, Clarissa Maria Mercedes Ford, John A. Abbafati, Cristiana Abdalla, Mohammed Altigani Abdelkader, Atef Abdi, Parsa Zuñiga, Roberto Ariel Abeldaño Abiodun, Olugbenga Olusola Abolhassani, Hassan Abu-Gharbieh, Eman Abukhadijah, Hana J. Abu-Zaid, Ahmed Addo, Isaac Yeboah Addolorato, Giovanni Adekanmbi, Victor Adetunji, Juliana Bunmi Adeyeoluwa, Temitayo Esther Agardh, Emilie E. Agyemang-Duah, Williams Ahmad, Danish Ahmed, Ayman Ahmed, Syed Anees Akinosoglou, Karolina Akkaif, Mohammed Ahmed Al Awaidy, Salah Al Hasan, Syed Mahfuz Al Zaabi, Omar Ali Mohammed Aldridge, Robert W. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Ali, Mohammed Usman Ali, Syed Shujait Ali, Waad Alicandro, Gianfranco Alif, Sheikh Mohammad Al-Jumaily, Adel Allebeck, Peter Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Alshahrani, Najim Z. Aluh, Deborah Oyine Al-Wardat, Mohammad Al-Zyoud, Walid A. Amiri, Sohrab Anderlini, Deanna Andrei, Catalina Liliana Anil, Abhishek Anvari, Saeid Anyasodor, Anayochukwu Edward Appiah, Seth Christopher Yaw Aquilano, Michele Arabloo, Jalal Arafat, Mosab Areda, Demelash Aremu, Abdulfatai Armani, Keivan Armocida, Benedetta Ärnlöv, Johan Asaduzzaman, Muhammad Astell-Burt, Thomas Aujayeb, Avinash Ausloos, Marcel Azadnajafabad, Sina Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Badache, Andreea Corina Badiye, Ashish D. Bahramian, Saeed Baig, Atif Amin Baker, Jennifer L. Bansal, Hansi Bärnighausen, Till Winfried Barone, Mark Thomaz Ugliara Barrow, Amadou Barteit, Sandra Bashir, Shahid Bashiru, Hameed Akande Basso, João Diogo Bastan, Mohammad-Mahdi Basu, Sanjay Batra, Kavita Bauckneht, Matteo Baune, Bernhard T. Beghi, Massimiliano Beiranvand, Maryam Béjot, Yannick Bell, Michelle L. Bello, Olorunjuwon Omolaja Belo, Luis Beloukas, Apostolos Beneke, Alice A. Bettencourt, Paulo J.G. Bhagavathula, Akshaya Srikanth Bhala, Neeraj Bhaskar, Sonu Bisulli, Francesca Bjørge, Tone Bodunrin, Aadam Olalekan Carvajal, Alejandro Botero Bouaoud, Souad Brayne, Carol Brenner, H Department of Primary Care and Public Health University of East Anglia Norwich United Kingdom Office for Health Improvement and Disparities Department of Health and Social Care London United Kingdom Department of Legal and Economic Studies La Sapienza University Rome Italy Rome Italy Hull York Medical School University of Hull Hull United Kingdom Department of Mathematics and Sciences Ajman University Ajman United Arab Emirates Department of Medicine Memorial University St. John's NL Canada Postgraduate Department University of Sierra Sur Miahuatlan de Porfirio Diaz Mexico Yhteiskuntadatatieteen keskus (Centre for Social Data Science) Helsinki Finland Department of Internal Medicine Federal Medical Centre Abuja Nigeria Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Department of Medical Biochemistry and Biophysics Karolinska Institutet (Karolinska Institute) Stockholm Sweden Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center Hamad Medical Corporation Doha Qatar Department of Biochemistry and Molecular Medicine Alfaisal University Riyadh Saudi Arabia College of Graduate Health Sciences University of Tennessee Memphis TN United States School of Medicine Sydney NSW Australia Centre for Social Research in Health University of New South Wales Sydney NSW Australia Internal Medicine and Alcohol Related Disease Unit Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Department of Medical and Surgical Sciences Università Cattolica di Roma (Catholic University of Rome) Rome Italy Department of Obstetrics and Gynecology University of Texas Medical Branch Galveston TX United States Department of Biochemistry Osun State University Osogbo Nigeria Department of Pharmacology and Therapeutics University of Medical Sciences Ondo Ondo Nigeria Department of Veteri

Background: Decades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factors and cause-specific death rates in different European countries related to changes in life expectancy in those countries before and during the COVID-19 pandemic. Methods: We used data and methods from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to compare changes in life expectancy at birth, causes of death, and population exposure to risk factors in 16 European Economic Area countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, and Sweden) and the four UK nations (England, Northern Ireland, Scotland, and Wales) for three time periods: 1990–2011, 2011–19, and 2019–21. Changes in life expectancy and causes of death were estimated with an established life expectancy cause-specific decomposition method, and compared with summary exposure values of risk factors for the major causes of death influencing life expectancy. Findings: All countries showed mean annual improvements in life expectancy in both 1990–2011 (overall mean 0·23 years [95% uncertainty interval [UI] 0·23 to 0·24]) and 2011–19 (overall mean 0·15 years [0·13 to 0·16]). The rate of improvement was lower in 2011–19 than in 1990–2011 in all countries except for Norway, where the mean annual increase in life expectancy rose from 0·21 years (95% UI 0·20 to 0·22) in 1990–2011 to 0·23 years (0·21 to 0·26) in 2011–19 (difference of 0·03 years). In other countries, the difference in mean annual improvement between these periods ranged from –0·01 years in Iceland (0·19 years [95% UI 0·16 to 0·21] vs 0·18 years [0·09 to 0·26]), to –0·18 years in England (0·25 years [0·24 to 0·25] vs 0·07 years [0·06 to 0·08]). In 2019–21, there was an overall decrease in mean annual life expectancy a

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Breed as One of Significant Factors that Determine the Level of Yield and Its Quality

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IOP Conference Series: Earth and Environmental science 2022年第4期988卷

作者： E V Matvienko A L Zolkin D K Suchkov L A Pankratova PhD in Biological Science Junior Researcher of the Laboratory of Breeding and Seed Farming of Cereal and Sorghum Crops Volga NIISS - branch of the Samara Scientific Center of the Russian Academy of Sciences Ph.D. in Engineering Science Senior Lecturer of Computer and Information Sciences Department of the Povolzhskiy State University of Telecommunications and Informatics Junior Researcher Federal Research Center for Agroecology Integrated Land Reclamation and Protective Afforestation of the Russian Academy of Sciences (Laboratory for Predicting the Bio-productivity of Agroforestscapes) PhD in Geography Senior Lecturer of the Physical Geography and Landscape Planning Department Institute of Earth Sciences St.Petersburg State University

Breed is studied in the article as of significant factors that determine the level of yield and its quality. World experience shows that the consistent increase in the yield of cultivated crops is based on the improvement of the cultivation technology and the achievements of breeding. The production of agricultural products is based on the variety of breeds. According to A.A. Zhuchenko, breed determines the basic requirements for cultivation technologies: productivity, energy efficiency, environmentally friendly quality and environmental protection. It is believed that 25% of the yield is determined by the genetic characteristics of the cultivated breed. The role of the genotype in increasing and stabilizing the yield is constantly increasing, and the contribution of the breed during zoning is estimated at 30 ... 50%.

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A fast intra mode decision method based on reduction of the number of modes in HEVC standard

A fast intra mode decision method based on reduction of the ...

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International Symposium on Telecommunications, IST

作者： Mohammadreza Ramezanpour Fini Farzad ZargariAsl PHD candidate in Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Information Technology Research Institute Iran Telecom Research Center (ITRC) Tehran Iran

ISBN: (纸本)9781479953608

High efficiency video coding (HEVC) standard, introduced by joint collaborative team on video coding (JCT-VC) is the newest international standard of video compression. This standard provides more compression and better video quality, compared to the previous standards such as H.264. The higher compression efficiency is obtained at the cost of an increase in the computational load. One of the portions which imposes high computational load to encoder is the intra prediction unit. In HEVC, 35 modes are proposed for intra prediction to improve the compression efficiency. To reduce the computational load of intra prediction, HEVC uses a pre-processing step, called Rough Mode Decision (RMD). It selects a number of best prediction modes. Then, using Rate Distortion Optimization (RDO) process, the encoder selects the best prediction mode. We reduced the number of the tested modes in RMD from 35 to 19 in our proposed method. Moreover, we reduced the number of the selected modes through the RMD step in order to decrease the encoding time. Simulation results indicate 14.2% reduction in the encoding time whereas the R-D specification in general remains unchanged.

关键词： Encoding Prediction algorithms Video coding Standards Software Algorithm design and analysis Computational complexity

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Numerical and Neural Network Modeling of Motors of a Robot

Numerical and Neural Network Modeling of Motors of a Robot

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RSI/ISM International Conference on Robotics and Mechatronics

作者： H. Tourajizadeh S. Manteghi S. R. Nekoo Assistant professor Department of Mechanical Engineering Faculty of Engineering Kharazmi University Tehran Iran M. Sc. College of Computer and Mechatronics Engineering Islamic Azad University Branch of Science and Research Tehran Iran PhD Student School of Mechanical Engineering Iran University of Science and Technology (IUST) Tehran Iran

ISBN: (纸本)9781467372350

In this paper, parametric and numerical model of the motors of a robot are extracted. A method is proposed here to control the torque and velocity of the motor simultaneously using the extracted dynamics of the motor and consequently control the robot motion more accurately. Parametric model of the motors are derived by conducting standard tests like locked rotor test and step and sine wave input test. In order to derive the neural network and numerical models, a set of sinusoidal, triangular, and random steps signal, are applied as the input to the motor and its speed is recorded as the output. Neural network model of the motors is extracted by using these dataset and considering the MLP neural network structure with Levenberg _Marquardt training method. Results of the numerical model and parametric models are compared and validated by experimental tests.

关键词： Robot Modeling of DC motor Torque-speed control Training algorithm Levenberg-Marquardt

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Effect of epsilon-based record selection on fragility curves of typical irregular steel frames with concrete shear walls in Mashhad city

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International Journal of Advanced Structural engineering 2013年第1期5卷 1-11页

作者： Kazemi, Hamid Ghafory-Ashtiany, Mohsen Azarbakht, Alireza PhD candidate Department of Civil Engineering Science and Research Branch Islamic Azad University Tehran Iran International Institute of Earthquake Engineering and Seismology (IIEES) No. 26 Arghavan Street Tehran 19395-3913 Iran Department of Civil Engineering Faculty of Engineering Arak University P.O. Box 38156-88359 Arak Iran

In this paper, the seismic vulnerability of Mashhad city, as the second largest city in Iran, has been investigated using analytical fragility curves. Disaggregation analysis is first performed in order to identify the target epsilon at different hazard levels. The disaggregation results revealed different epsilon values at the first mode period of two representative structures, in the case of 72-, 475-, and 2,475-year return periods. Nonlinear incremental dynamic analyses are then performed for two representative models of a typical steel frame with a concrete shear wall, using independent suites of acceleration time histories that are selected based on the target epsilons. Structural limit states are defined on each incremental dynamic analysis curve, and the corresponding damage measures are estimated. The results show that if ϵ is neglected in the considered simulations, then the predicted median structural capacities is decreased by around 10%, 15%, and 18%, respectively, for the three abovementioned hazard levels. © 2013, Kazemi et al.;licensee Springer.

关键词： 3D analysis Collapse capacity Epsilon Hazard disaggregation Record selection Spectral shape Strong ground motion Vulnerability

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