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Accurate Discharge Coefficient Prediction of Streamlined Weirs by Coupling Linear Regression and Deep Convolutional Gated Recurrent Unit

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arXiv 2022年

作者： Chen, Weibin Sharifrazi, Danial Liang, Guoxi Band, Shahab S. Chau, Kwok Wing Mosavi, Amir College of Computer Science and Artificial Intelligence Wenzhou University Zhejiang Wenzhou325035 China Department of Computer Engineering Shiraz Branch Islamic Azad University Shiraz Iran Department of Artificial Intelligence Wenzhou Polytechnic Wenzhou325035 China Future Technology Research Center College of Future National Yunlin University of Science and Technology Yunlin County Taiwan Department of Civil and Environmental Engineering Hong Kong Polytechnic University Hong Kong John von Neumann Faculty of Informatics Obuda University Budapest Hungary Institute of Information Engineering Automation and Mathematics Slovak University of Technology Bratislava Slovakia

Streamlined weirs, as a nature-inspired type of weirs, has gained tremendous attention among hydraulic engineers mainly due to their well-known performance with high discharge coefficient. Computational fluid dynamic (CDF) is considered as a robust tool to predict discharge coefficient. To bypass the computational cost of CFD-based assessment, the present study proposes data-driven modelling techniques, as an alternative to CFD simulation, to predict discharge coefficient based on an experimental dataset. To this end, after splitting the dataset by k-fold cross-validation technique, the performance assessment of classical and hybrid machine-deep learning (ML-DL) algorithms is undertaken. Amongst ML techniques, linear regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbours (KNN), and decision tree (DT) algorithms are studied . In the context of DL, long short-term memory (LSTM), convolutional neural network (CNN), gated recurrent unit (GRU) and their hybrid forms such as LSTM-GRU, CNN-LSTM and CNN-GRU techniques are compared by different error metrics. It is found that the proposed three-layer hierarchical DL algorithm consisting of a convolutional layer coupled with two subsequent GRU levels, which is also hybridized by LR method (i.e., LR-CGRU), leads to lower error metrics. This paper paves the way for data-driven modelling of streamlined *** Codes 68T05 © 2022, CC BY.

关键词： Deep neural networks

Linearity assessment between lower limb joint angles and angular accelerations at standard maximum vertical jumps with long-short and no countermovement

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Gait & Posture 2023年 106卷 S174-S175页

作者： Rodrigues, Carlos Correia, Miguel Abrantes, João Benedetti, Marco Nadal, Jurandir Faculty of Engineering- University of Porto Biomedical Engineering Program Porto Portugal INESC TEC - Institute for Systems and Computer Engineering- Technology and Science C-BER - Centre for Biomedical Engineering Research Porto Portugal Faculty of Engineering- University of Porto Department of Electrical and Computer Engineering Porto Portugal CICANT - Centre for Research in Applied Communication- Culture and New Technologies MovLab - Laboratory of Technologies for Interactions and Interfaces Lisbon Portugal Federal University of Pernambuco Department of Electronic and Systems Recife Brazil Federal University of Rio de Janeiro Biomedical Engineering Program Rio de Janeiro Brazil

IntroductionStandard maximum vertical jump (MVJ) has been applied to assess lower limb long countermovement (CM) on countermovement jump (CMJ) and short CM on drop jump (DJ) for comparison to squat jump (SJ) without CM [1,2]. research questionNevertheless, the ability for lower limb joint accelerations depending on lower limb joint angles at long, short and no CM remains an open issue [3,4], in particular linearity assessment of these relations at long, short and no CM in association with MVJ whole-body impulsion. MethodsFor this reason, we applied piecewise linear detection of segmented subphases on lower limb joint angles ( θ)-angular acceleration ( α) diagrams for the hip (H), knee (K) and ankle (A) during impulse phases on CMJ, DJ and SJ. Selected sample corresponds to best MVJ trials based on higher flight-time of 3 CMJ, DJ and SJ repetitions for six healthy untrained subjects with ages (21.5±1.4) years, (76.7±9.3)kg mass and (1.79±0.06)m height. Adhesive reflective markers were applied at lower limb and torso, with sagittal hip, knee, and ankle θ, α obtained by inverse kinematics. H,K,A ( θ, α) diagrams were plotted and impulsion subphases segmented at selected instants of ∂ a/∂ q phase reversal with linear fit by the least-squares method, Fig. 1. Fig. 1. Hip, knee, ankle joint ( θ, α) diagrams at CMJ, DJ, SJ with segmented subphases time span(Δt), |∂α/∂θ| slope magnitudes and R² linear fit regression. ResultsSJ presented larger number of ( θ, α) subphases than DJ, both larger than CMJ with higher |∂ α/∂ θ| variation at DJ and SJ than CMJ, pointing to higher complexity of neuromuscular control and contributing to explain reduced SJ, DJ performance without CM (0.33±0.05)m and with short CM (0.27±0.03)m in relation to CMJ with long CM (0.36±0.04)m for untrained selected subjects, Fig. 1. Thus, despite the larger total time span Δt at CMJ (0.76±0.10)s in relation to SJ (0.36±0.08)s and DJ (0.23±0.05)s, CMJ presented lower number of subphases with longer time span

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Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learning

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arXiv 2022年

作者： Talukder, Md Alamin Islam, Md Manowarul Uddin, Md Ashraf Akhter, Arnisha Hasan, Khondokar Fida Moni, Mohammad Ali Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Centre for Cyber Security Research & Innovation RMIT University 124 La Trobe Street MelbourneVIC3000 Australia Artificial Intelligence & Data Science School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St LuciaQLD4072 Australia

Cancer is a fatal disease caused by a combination of genetic diseases and a variety of biochemical abnormalities. Lung and colon cancer have emerged as two of the leading causes of death and disability in humans. The histopathological detection of such malignancies is usually the most important component in determining the best course of action. Early detection of the ailment on either front considerably decreases the likelihood of mortality. Machine learning and deep learning techniques can be utilized to speed up such cancer detection, allowing researchers to study a large number of patients in a much shorter amount of time and at a lower cost. In this research work, we introduced a hybrid ensemble feature extraction model to efficiently identify lung and colon cancer. It integrates deep feature extraction and ensemble learning with high-performance filtering for cancer image datasets. The model is evaluated on histopathological (LC25000) lung and colon datasets. According to the study findings, our hybrid model can detect lung, colon, and (lung and colon) cancer with accuracy rates of 99.05%, 100%, and 99.30%, respectively. The study's findings show that our proposed strategy outperforms existing models significantly. Thus, these models could be applicable in clinics to support the doctor in the diagnosis of cancers. © 2022, CC BY.

关键词： Feature extraction

Cosmic strings, inflation, and gravity waves

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arXiv 2021年

作者： Lazarides, George Maji, Rinku Shafi, Qaisar School of Electrical and Computer Engineering Faculty of Engineering Aristotle University of Thessaloniki Thessaloniki54124 Greece Department of Physics Indian Institute of Technology Kanpur Kanpur208016 India Bartol Research Institute Department of Physics and Astronomy University of Delaware NewarkDE19716 United States

We investigate the impact of Coleman-Weinberg inflation on the stochastic gravity wave background spectrum emitted by intermediate scale cosmic strings. The string network is partially inflated and re-enters the horizon at later times after the end of inflation, such that the short string loops are not produced. This leads to a significant modification of the gravity wave spectrum that we explore in detail. We find that Coleman-Weinberg inflation can help to satisfy the Parkes Pulsar Timing Array (PPTA) bound for dimensionless string tension values in the range Gµ > 1.1×10−10. We also identify the modified gravity wave spectra which, in the case of inflation, are compatible with the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) data. We then discuss the formation of monopoles and strings at the same breaking scale and the compatibility of the Monopole, Astrophysics and Cosmic Ray Observatory (MACRO) bound with the PPTA bound, and also with the NANOGrav data. Finally, an example of a realistic non-supersymmetric E6 model incorporating successful Coleman-Weinberg inflation is presented in which monopoles and strings both survive inflation and are present at an observable level. Copyright © 2021, The Authors. All rights reserved.

关键词： Gravity waves

An Overview and Experimental Study of Learning-based Optimization Algorithms for Vehicle Routing Problem

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arXiv 2021年

作者： Li, Bingjie Wu, Guohua He, Yongming Fan, Mingfeng Pedrycz, Witold The School of Traffic and Transportation Engineering Central South University Changsha410075 China The College of Systems Engineering National University of Defense Technology Changsha410073 China The Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2V4 Canada The Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia The Systems Research Institute Polish Academy of Sciences Warsaw01447 Poland

Vehicle routing problem (VRP) is a typical discrete combinatorial optimization problem, and many models and algorithms have been proposed to solve the VRP and its variants. Although existing approaches have contributed a lot to the development of this field, these approaches either are limited in problem size or need manual intervening in choosing parameters. To solve these difficulties, many studies have considered the learning-based optimization (LBO) algorithms to solve the VRP. This paper reviews recent advances in this field and divides relevant approaches into end-to-end approaches and step-by-step approaches. We performed a statistical analysis of the reviewed articles from various aspects and designed three experiments to evaluate the performance of four representative LBO algorithms. Finally, we conclude the applicable types of problems for different LBO algorithms and suggest directions in which researchers can improve LBO algorithms. Copyright © 2021, The Authors. All rights reserved.

关键词： Vehicle routing

Evaluation of China’s Digital Economy: A Case Study using Entropy and TOPSIS

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Procedia computer Science 2024年 242卷 1256-1262页

作者： Junjie Liu Boya Wang Jiayu Xue Yi Qu Yong Shi Sino-Danish College University of Chinese Academy of Sciences Beijing 100049 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Faculty of Science and Engineering University of Nottingham Ningbo China Ningbo Zhejiang 315100 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 101408 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha Omaha NE 68182 USA

Recently, digital economy in social science has become increasingly significant. A well-constructed evaluation structure could generally and promptly describe and reveal its overall social digitalization development. Building on existent researches, this paper improves the evaluation system, which comprises four first-level indicators and thirty-one second-level indicators. Notably, this paper innovatively incorporates higher education into digital innovation of the evaluation. The Entropy method is utilized to assign weights to various indicators in an objective way, with employing the TOPSIS approach to prioritize the development of digital economy in places based on the closeness to the ideal solution. By analysing related data related from 31 provinces in China between 2017 to 2021, this paper greatly finds: Firstly, the development of digital economy vary significantly across provinces, with most provinces at a relatively poor level, indicating considerable potential for overall growth; Secondly, Guangdong, Jiangsu, Zhejiang, and Shandong consistently ranked in the top four from 2017 to 2021, reflecting their specific development attributes and their emphasis on digital economy; Thirdly, significant disparities exist both between regions and among provinces. Consequently, this paper suggests establishing a comprehensive national digital economy development plan, formulating region-specific digital economy policies tailored to local conditions, and encouraging leading provinces to actively share experiences with other provinces.

关键词： Digital economy Multi-source information Evaluation TOPSIS

A novel hybrid machine learning approach for δ¹³C spatial prediction in polish hard-water lakes

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Ecological Informatics 2025年 89卷

作者： Shahabi, Himan Shirzadi, Ataollah Ustrzycka, Alicja Piotrowska, Natalia Filipiak, Janusz Tahan, Marzieh Hajizadeh Division of Geochronology and Environmental Isotopes Institute of Physics Silesian University of Technology Gliwice 44-100 Poland Departments of Geomorphology Faculty of Natural Resources University of Kurdistan Sanandaj Iran Department of Rangeland and Watershed Management Faculty of Natural Resources University of Kurdistan Sanandaj Iran Department of Physical Oceanography and Climate Research Faculty of Oceanography and Geography University of Gdańsk Gdańsk Poland Department of Computer Engineering Faculty of Engineering Meybod University Meybod Iran

Comprehension of carbon cycling, climate change, paleoecology, environmental reconstruction, and aquatic ecosystem health is essential for the environmental sciences, and one of the invaluable tools is the δ¹³C record of lake deposits. In this study, we propose a novel hybrid machine learning (ML) algorithm known as the ARAMT model, which combines two key components: a meta-classifier of additive regression (AR) and a base classifier of alternating model trees (AMT). The AR-AMT hybrid model significantly enhances the prediction of carbon isotopes (δ¹³C) by addressing limitations in existing methodologies. For the first time, this model is used to predict the spatial prediction of a stable isotope in Polish lakes. The δ¹³C for 30 Polish lakes is determined using the calcite (CaCO₃) precipitated in the near-surface layers of the lakes (epilimnion). The chemical composition (Ca²⁺, HCO₃⁻, Na, K, sulfates, fluorides, Cl, Mg, and P) and temperature of the surface water at a depth of 1 m is analyzed seasonally. The current approaches for predicting δ¹³C have demonstrated shortcomings in accuracy and precision. In this study, a random forest (RF), M5P, AMT, and Gaussian process (GP) are the four cutting-edge ML algorithms that are compared with the proposed hybrid model (ARAMT). In accordance with the results, the ARAMT hybrid model performed more effectively in predicting δ¹³C than the other benchmark ML methods (R² = 0.9882, MAE = 0.456, and RMSE = 0.527), the others giving: AMT (R² = 0.982, MAE = 0.558, and RMSE = 0.347), RF (R² = 0.8014, MAE = 0.612, and RMSE = 0.550), M5P (R² = 0.7508, MAE = 0.813, and RMSE = 0.701), and GP (R² = 0.7315, MAE = 0.768, and RMSE = 0.683). Although the ARAMT hybrid doesn't directly preserve lake ecosystems, the enhanced accuracy of its δ¹³C predictions by providing a more detai

关键词： Aquatic ecosystem health Carbon cycling Carbon-13 predicting Climate change Machine learning algorithms Polish lakes ecology

Enhancing Data Security for Cloud Computing Applications through Distributed Blockchain-based SDN Architecture in IoT Networks

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arXiv 2022年

作者： Rahman, Anichur Islam, M.D. Jahidul Islam, Rafiqul Kundu, Dipanjali Karim, M.D. Razaul Rahman, Ziaur Band, Shahab S. Constituent Institute of the University of Dhaka Savar Dhaka1350 Bangladesh Department of Computer Science and Engineering Mawlana Bhashani Science and Technology University Tangail Bangladesh Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh School of Computing and Mathematics Faculty of Business Justice and Behavioural Sciences Charles Sturt University Australia Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Tangail Bangladesh Future Technology Research Center College of Future National Yunlin University of Science and Technology Douliou Taiwan

Blockchain (BC) and Software Defined Networking (SDN) are some of the most prominent emerging technologies in recent research. These technologies provide security, integrity, as well as confidentiality in their respective applications. Cloud computing has also been a popular comprehensive technology for several years. Confidential information is often shared with the cloud infrastructure to give customers access to remote resources, such as computation and storage operations. However, cloud computing also presents substantial security threats, issues, and challenges. Therefore, to overcome these difficulties, we propose integrating Blockchain and SDN in the cloud computing platform. In this research, we introduce the "DistB-SDCloud" architecture to better secure clouds. Moreover, we leverage a distributed Blockchain approach to convey security, confidentiality, privacy, integrity, adaptability, and scalability in the proposed architecture. BC provides a distributed or decentralized and efficient environment for users. Also, we present an SDN approach to improving the reliability, stability, and load balancing capabilities of the cloud infrastructure. Finally, we provide an experimental evaluation of the performance of our SDN and BC-based implementation using different parameters, also monitoring some attacks in the system and proving its efficacy. © 2022, CC BY-NC-SA.

关键词： Blockchain

Income inequality and mobility in geometric Brownian motion with stochastic resetting: Theoretical results and empirical evidence of non-ergodicity

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arXiv 2021年

作者： Stojkoski, Viktor Jolakoski, Petar Pal, Arnab Sandev, Trifce Kocarev, Ljupco Metzler, Ralf Faculty of Economics Ss. Cyril and Methodius University Skopje1000 Macedonia Research Center for Computer Science and Information Technologies Macedonian Academy of Sciences and Arts Association for Research and Analysis - ZMAI Skopje1000 Macedonia Department of Physics Indian Institute of Technology Kanpur Kanpur208016 India Institute of Physics & Astronomy University of Potsdam Institute of Physics Faculty of Natural Sciences and Mathematics Ss Cyril and Methodius University Faculty of Computer Science and Engineering Ss. Cyril and Methodius University

We explore the role of non-ergodicity in the relationship between income inequality, the extent of concentration in the income distribution, and mobility, the feasibility of an individual to change their position in the income distribution. For this purpose, we explore the properties of an established model for income growth that includes "resetting" as a stabilising force which ensures stationary dynamics. We find that the dynamics of inequality is regime-dependent and may range from a strictly non-ergodic state where this phenomenon has an increasing trend, up to a stable regime where inequality is steady and the system efficiently mimics ergodic behaviour. Mobility measures, conversely, are always stable over time, but the stationary value is dependent on the regime, suggesting that economies become less mobile in non-ergodic regimes. By fitting the model to empirical data for the dynamics of income share of the top earners in the United States, we provide evidence that the income dynamics in this country is consistently in a regime in which non-ergodicity characterises inequality and immobility dynamics. Our results can serve as a simple rationale for the observed real world income dynamics and as such aid in addressing non-ergodicity in various empirical settings across the globe. Copyright © 2021, The Authors. All rights reserved.

关键词： Dynamics