Shaowei Wang^1,2, Cong Xu¹, Hao Gu^3,*, Pinghua Zhu¹, Hui Liu¹, Bo Xu⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 97-117, 2022, DOI:10.32604/cmes.2022.018721

Abstract Many concrete dams seriously suffer from long-term seepage dissolution, and the induced mechanical property deterioration of concrete may significantly affect the structural performance, especially the seismic safety. An approach is presented in this paper to quantify the influence of seepage dissolution on seismic performance of concrete dams. To connect laboratory test with numerical simulation, dissolution tests are conducted for concrete specimens and using the cumulative relative leached calcium as an aging index, a deterioration model is established to predict the mechanical property of leached concrete in the first step. A coupled seepage-calcium dissolution-migration model containing two calculation modes is proposed… More >

Songlin Yang^1,2, Xingjin Han^1,2, Chufeng Kuang^1,2, Weihua Fang³, Jianfei Zhang⁴, Tiantang Yu^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 49-72, 2022, DOI:10.32604/cmes.2022.018325

Abstract The deformation prediction models of Wuqiangxi concrete gravity dam are developed, including two statistical models and a deep learning model. In the statistical models, the reliable monitoring data are firstly determined with Lahitte criterion; then, the stepwise regression and partial least squares regression models for deformation prediction of concrete gravity dam are constructed in terms of the reliable monitoring data, and the factors of water pressure, temperature and time effect are considered in the models; finally, according to the monitoring data from 2006 to 2020 of five typical measuring points including J₂₃ (on dam section ), J₃₃ (on dam section… More >

R. Kanthavel^1,*, R. Dhaya²

Computer Systems Science and Engineering, Vol.42, No.1, pp. 257-269, 2022, DOI:10.32604/csse.2022.021606

Abstract Osteoarthritis is the most common class of arthritis that involves tears down the soft cartilage between the joints of the knee. The regeneration of this cartilage tissue is not possible, and thus physicians typically suggest therapeutic measures to prevent further deterioration over time. Normally, bringing about joint replacement is a remedial course of action. Expose itself in joint pain recognized with a normal X-ray. Deep learning plays a vital role in predicting the early stages of osteoarthritis by using the MRI pictures of muscles of the knee muscle. It can be used to accurately measure the shape and texture of… More >

Qihui Ling^1,2,*, Wei Zhang², Qiancheng Zhao^1,2, Juchuan Dai^1,2

Energy Engineering, Vol.119, No.1, pp. 287-300, 2022, DOI:10.32604/EE.2022.014718

Abstract The inverter is one of the key components of wind turbine, and it is a complex circuit composed of a series of components such as a variety of electronic components and power devices. Therefore, it is difficult to accurately identify the operation states of inverter and some problems regarding its own circuit, especially in the early stages of failure. However, if the inverter temperature prediction model can be established, the early states can be identified through the judgment of the output temperature. Accordingly, considering whether the inverter heats up normally from the perspective of heat dissipation, a method for the… More >

Ashraf Ahmed¹, Yousef AbuHour^2,*, Ammar El-Hassan¹

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 979-990, 2022, DOI:10.32604/iasc.2022.020726

Abstract The Corona (COVID-19) epidemic has triggered interest in many fields of technology, medicine, science, and politics. Most of the mathematical research in this area focused on analyzing the dynamics of the spread of the virus. In this article, after a review of some current methodologies, a non-linear system of differential equations is developed to model the spread of COVID-19. In order to consider a wide spectrum of scenarios, we propose a susceptible-exposed-infected-quarantined-recovered (SEIQRS)-model which was analyzed to determine threshold conditions for its stability, and the number of infected cases that is an infected person will transmit on a virus to,… More >

Mesfer Al Duhayyim¹, Hadeel Alsolai², Fahd N. Al-Wesabi^3,4, Nadhem Nemri³, Hany Mahgoub³, Anwer Mustafa Hilal⁵, Manar Ahmed Hamza^5,*, Mohammed Rizwanullah⁵

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1079-1094, 2022, DOI:10.32604/cmc.2022.021199

Abstract Recently, Financial Technology (FinTech) has received more attention among financial sectors and researchers to derive effective solutions for any financial institution or firm. Financial crisis prediction (FCP) is an essential topic in business sector that finds it useful to identify the financial condition of a financial institution. At the same time, the development of the internet of things (IoT) has altered the mode of human interaction with the physical world. The IoT can be combined with the FCP model to examine the financial data from the users and perform decision making process. This paper presents a novel multi-objective squirrel search… More >

Ayesha Siddiqa¹, Syed Abbas Zilqurnain Naqvi¹, Muhammad Ahsan¹, Allah Ditta², Hani Alquhayz³, M. A. Khan⁴, Muhammad Adnan Khan^5,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5519-5536, 2022, DOI:10.32604/cmc.2022.021666

Abstract Due to unforeseen climate change, complicated chronic diseases, and mutation of viruses’ hospital administration’s top challenge is to know about the Length of stay (LOS) of different diseased patients in the hospitals. Hospital management does not exactly know when the existing patient leaves the hospital; this information could be crucial for hospital management. It could allow them to take more patients for admission. As a result, hospitals face many problems managing available resources and new patients in getting entries for their prompt treatment. Therefore, a robust model needs to be designed to help hospital administration predict patients’ LOS to resolve… More >

P. Thamizhazhagan^1,*, M. Sujatha², S. Umadevi³, K. Priyadarshini⁴, Velmurugan Subbiah Parvathy⁵, Irina V. Pustokhina⁶, Denis A. Pustokhin⁷

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3333-3347, 2022, DOI:10.32604/cmc.2022.020197

Abstract There is a paradigm shift happening in automotive industry towards electric vehicles as environment and sustainability issues gained momentum in the recent years among potential users. Connected and Autonomous Electric Vehicle (CAEV) technologies are fascinating the automakers and inducing them to manufacture connected autonomous vehicles with self-driving features such as autopilot and self-parking. Therefore, Traffic Flow Prediction (TFP) is identified as a major issue in CAEV technologies which needs to be addressed with the help of Deep Learning (DL) techniques. In this view, the current research paper presents an artificial intelligence-based parallel autoencoder for TFP, abbreviated as AIPAE-TFP model in… More >

Mamoona Humayun^1,*, Ahmed Alsayat²

Computer Systems Science and Engineering, Vol.40, No.3, pp. 947-961, 2022, DOI:10.32604/csse.2022.019288

Abstract The recent global outbreak of COVID-19 damaged the world health systems, human health, economy, and daily life badly. None of the countries was ready to face this emerging health challenge. Health professionals were not able to predict its rise and next move, as well as the future curve and impact on lives in case of a similar pandemic situation happened. This created huge chaos globally, for longer and the world is still struggling to come up with any suitable solution. Here the better use of advanced technologies, such as artificial intelligence and deep learning, may aid healthcare practitioners in making… More >

R. Kingsy Grace^1,*, R. Manimegalai²

Computer Systems Science and Engineering, Vol.40, No.2, pp. 593-606, 2022, DOI:10.32604/csse.2022.019240

Abstract The prediction of wind speed is imperative nowadays due to the increased and effective generation of wind power. Wind power is the clean, free and conservative renewable energy. It is necessary to predict the wind speed, to implement wind power generation. This paper proposes a new model, named WT-GWO-BPNN, by integrating Wavelet Transform (WT), Back Propagation Neural Network (BPNN) and Grey Wolf Optimization (GWO). The wavelet transform is adopted to decompose the original time series data (wind speed) into approximation and detailed band. GWO – BPNN is applied to predict the wind speed. GWO is used to optimize the parameters… More >