Nidhi Parashar¹, Prashant Johri¹, Arfat Ahmad Khan⁵, Nitin Gaur¹, Seifedine Kadry^2,3,4,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 389-425, 2024, DOI:10.32604/cmc.2024.050240 - 18 July 2024

Abstract The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction research. Deep learning (DL) and machine learning (ML) models effectively deal with such challenges. This research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March 2024. In addition, it analyses the effectiveness of various input parameters considered in crop yield prediction models. We conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop yield. The… More >

Doaa Sami Khafaga¹, El-Sayed M. El-kenawy², Amel Ali Alhussan^1,*, Marwa M. Eid³

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2117-2132, 2023, DOI:10.32604/iasc.2023.038811 - 21 June 2023

Abstract The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and governments. Meanwhile, the accurate prediction can be realized using the recent advances in machine learning and predictive models. This research proposes a novel approach for energy consumption forecasting based on a new optimization algorithm and a new forecasting model consisting of a set of long short-term memory (LSTM) units. The proposed optimization algorithm is used to optimize the parameters of the LSTM-based model to boost its forecasting accuracy. This optimization algorithm is based on the recently emerged… More >

Salah Alzghoul^1,*, Othman Smadi¹, Ali Al Bataineh², Mamon Hatmal³, Ahmad Alamm⁴

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 455-470, 2023, DOI:10.32604/iasc.2023.038338 - 29 April 2023

Abstract Currently, the decision of aortic valve replacement surgery time for asymptomatic patients with moderate-to-severe aortic stenosis (AS) is made by healthcare professionals based on the patient’s clinical biometric records. A delay in surgical aortic valve replacement (SAVR) can potentially affect patients’ quality of life. By using ML algorithms, this study aims to predict the optimal SAVR timing and determine the enhancement in moderate-to-severe AS patient survival following surgery. This study represents a novel approach that has the potential to improve decision-making and, ultimately, improve patient outcomes. We analyze data from 176 patients with moderate-to-severe aortic… More >

Nada Yamoul^1,*, Latifa Dlimi¹, Baraka Achraf Chakir²

Energy Engineering, Vol.119, No.5, pp. 2135-2148, 2022, DOI:10.32604/ee.2022.020005 - 21 July 2022

Abstract The building sector is one of the main energy-consuming sectors in Morocco. In fact, it accounts for 33% of the final consumption of energy and records a high increase in the annual consumption of energy caused by further planned large-scale projects. Indeed, the energy consumption of the building sector is experiencing a significant acceleration justified by the rapid need for the development of housing stock, wich is estimated at an average increase of 1,5% per year; furthermore, tant is an estimated increase of about 6,4%. In this sense, building constitutes an important potential source for… 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 - 24 January 2022

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 ),… More >

Imran Ashraf¹, Waleed S. Alnumay², Rashid Ali³, Soojung Hur¹, Ali Kashif Bashir⁴, Yousaf Bin Zikria^1,*

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3009-3044, 2021, DOI:10.32604/cmc.2021.015140 - 01 March 2021

Abstract The COVID-19 pandemic has caused hundreds of thousands of deaths, millions of infections worldwide, and the loss of trillions of dollars for many large economies. It poses a grave threat to the human population with an excessive number of patients constituting an unprecedented challenge with which health systems have to cope. Researchers from many domains have devised diverse approaches for the timely diagnosis of COVID-19 to facilitate medical responses. In the same vein, a wide variety of research studies have investigated underlying medical conditions for indicators suggesting the severity and mortality of, and role of… More >

Dipeshkumar R. Sonaviya^*, Bhaven N. Tandel

Sound & Vibration, Vol.55, No.1, pp. 57-68, 2021, DOI:10.32604/sv.2021.010715 - 19 January 2021

Abstract Road traffic noise pollution has been recognized as a serious issue which affects human health as well as affects urban regions. Noise maps are very beneficial to identify the impact of noise pollution. A noise mapping study performed to study the propagation of noise in tier-II city along with field measurements. The noise maps are developed using a computer simulation model (SoundPLAN essential 4.0 software). The noise prediction models like U.K’s CoRTN, Germany’s RLS-90, and their modified versions, which can be used for homogenous road traffic conditions, cannot be successfully applied in heterogeneous road traffic… More >

Mohammad Aldossary^*

Computer Systems Science and Engineering, Vol.36, No.2, pp. 353-368, 2021, DOI:10.32604/csse.2021.014974 - 05 January 2021

Abstract With the expansion of cloud computing, optimizing the energy efficiency and cost of the cloud paradigm is considered significantly important, since it directly affects providers’ revenue and customers’ payment. Thus, providing prediction information of the cloud services can be very beneficial for the service providers, as they need to carefully predict their business growths and efficiently manage their resources. To optimize the use of cloud services, predictive mechanisms can be applied to improve resource utilization and reduce energy-related costs. However, such mechanisms need to be provided with energy awareness not only at the level of More >

Satoshi Minamoto*, Takuya Kadohira, Kaita Ito, Makoto Watanabe, Masahiko Demura

The International Conference on Computational & Experimental Engineering and Sciences, Vol.22, No.2, pp. 136-136, 2019, DOI:10.32604/icces.2019.05447

Abstract The Materials Integration (MI) System is a domestically developed system in the “Cross-ministerial Strategic Innovation Promotion Program” to analyze structural materials performance. The performance on structural materials having complicated inputs/outputs would be solved with the combination of different scientific programs or data from experiment. One of the merits of constructing a combined model (here we call workflow) is that calculations are performed and the data would be stored in the system automatically.
Furthermore, we developed a web application (“MIREA”: MI REgression Analyzer) that enables us to build high versatile prediction models based on machine learning… More >