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  • Open Access


    Genetic algorithm-optimized backpropagation neural network establishes a diagnostic prediction model for diabetic nephropathy: Combined machine learning and experimental validation in mice


    BIOCELL, Vol.47, No.6, pp. 1253-1263, 2023, DOI:10.32604/biocell.2023.027373

    Abstract Background: Diabetic nephropathy (DN) is the most common complication of type 2 diabetes mellitus and the main cause of end-stage renal disease worldwide. Diagnostic biomarkers may allow early diagnosis and treatment of DN to reduce the prevalence and delay the development of DN. Kidney biopsy is the gold standard for diagnosing DN; however, its invasive character is its primary limitation. The machine learning approach provides a non-invasive and specific criterion for diagnosing DN, although traditional machine learning algorithms need to be improved to enhance diagnostic performance. Methods: We applied high-throughput RNA sequencing to obtain the genes related to DN tubular… More >

  • Open Access


    Prediction Model of Drilling Costs for Ultra-Deep Wells Based on GA-BP Neural Network

    Wenhua Xu1,3, Yuming Zhu2, Yingrong Wei2, Ya Su2, Yan Xu1,3, Hui Ji1, Dehua Liu1,3,*

    Energy Engineering, Vol.120, No.7, pp. 1701-1715, 2023, DOI:10.32604/ee.2023.027703

    Abstract Drilling costs of ultra-deep well is the significant part of development investment, and accurate prediction of drilling costs plays an important role in reasonable budgeting and overall control of development cost. In order to improve the prediction accuracy of ultra-deep well drilling costs, the item and the dominant factors of drilling costs in Tarim oilfield are analyzed. Then, those factors of drilling costs are separated into categorical variables and numerous variables. Finally, a BP neural network model with drilling costs as the output is established, and hyper-parameters (initial weights and bias) of the BP neural network is optimized by genetic… More >

  • Open Access


    Machine Learning Prediction Models of Optimal Time for Aortic Valve Replacement in Asymptomatic Patients

    Salah Alzghoul1,*, 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

    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 stenosis who had undergone or… More >

  • Open Access


    Hyperparameter Optimization Based Deep Belief Network for Clean Buses Using Solar Energy Model

    Shekaina Justin1,*, Wafaa Saleh1,2, Tasneem Al Ghamdi1, J. Shermina3

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1091-1109, 2023, DOI:10.32604/iasc.2023.032589

    Abstract Renewable energy has become a solution to the world’s energy concerns in recent years. Photovoltaic (PV) technology is the fastest technique to convert solar radiation into electricity. Solar-powered buses, metros, and cars use PV technology. Such technologies are always evolving. Included in the parameters that need to be analysed and examined include PV capabilities, vehicle power requirements, utility patterns, acceleration and deceleration rates, and storage module type and capacity, among others. PVPG is intermittent and weather-dependent. Accurate forecasting and modelling of PV system output power are key to managing storage, delivery, and smart grids. With unparalleled data granularity, a data-driven… More >

  • Open Access


    Tensile Properties and Prediction Model of Recombinant Bamboo at Different Temperatures

    Kunpeng Zhao, Yang Wei*, Si Chen, Kang Zhao, Mingmin Ding

    Journal of Renewable Materials, Vol.11, No.6, pp. 2695-2712, 2023, DOI:10.32604/jrm.2023.025711

    Abstract The destruction of recombinant bamboo depends on many factors, and the complex ambient temperature is an important factor affecting its basic mechanical properties. To investigate the failure mechanism and stress–strain relationship of recombinant bamboo at different temperatures, eighteen tensile specimens of recombinant bamboo were tested. The results showed that with increasing ambient temperature, the typical failure modes of recombinant bamboo were flush fracture, toothed failure, and serrated failure. The ultimate tensile strength, ultimate strain and elastic modulus of recombinant bamboo decreased with increasing temperature, and the ultimate tensile stress decreased from 154.07 to 96.55 MPa, a decrease of 37.33%, and the ultimate… More > Graphic Abstract

    Tensile Properties and Prediction Model of Recombinant Bamboo at Different Temperatures

  • Open Access



    Rongge Xiaoa,* , Qi Zhuanga, Shuaishuai Jina , Wenbo Jina

    Frontiers in Heat and Mass Transfer, Vol.18, No.1, pp. 1-7, 2022, DOI:10.5098/hmt.18.8

    Abstract A model for predicting wax deposition rate in pipeline transportation is constructed to predict wax deposition in actual pipeline, which can provide decision support for the flow guarantee of waxy crude oil in pipeline transportation. This paper analyzes the working principle of Back Propagation Neural Networks (BPNN). Aiming at the problems of BPNN model, such as over learning, long training time, low generalization ability and easy to fall into local minimum, the paper proposes an improved scheme of using Whale Optimization Algorithm (WOA) to optimize BPNN model(WOABPNN).Taking 38 groups of crude oil wax deposition experimental data in Huachi operation area… More >

  • Open Access



    Qi Zhuanga,* , Dong Liub, Bo Liuc, Mei Liua

    Frontiers in Heat and Mass Transfer, Vol.20, No.1, pp. 1-6, 2023, DOI:10.5098/hmt.20.13

    Abstract In the actual operation of wet gas pipeline, liquid accumulation is easy to form in the low-lying and uphill sections of the pipeline, which leads to a series of problems such as reduced pipeline transportation efficiency, increased pipeline pressure drop, hydrate formation, slug flow and intensified corrosion in the pipeline. Accurate calculation of liquid holdup is of great significance to the research of flow pattern identification, pipeline corrosion evaluation and prediction, and gas pipeline transportation efficiency calculation. Based on the experimental data of liquid holdup in horizontal pipeline, a commonly used BP neural network (BPNN) model is established in this… More >

  • Open Access


    SMOGN, MFO, and XGBoost Based Excitation Current Prediction Model for Synchronous Machine

    Ping-Huan Kuo1,2, Yu-Tsun Chen1, Her-Terng Yau1,2,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2687-2709, 2023, DOI:10.32604/csse.2023.036293

    Abstract The power factor is the ratio between the active and apparent power, and it is available to determine the operational capability of the intended circuit or the parts. The excitation current of the synchronous motor is an essential parameter required for adjusting the power factor because it determines whether the motor is under the optimal operating status. Although the excitation current should predict with the experimental devices, such a method is unsuitable for online real-time prediction. The artificial intelligence algorithm can compensate for the defect of conventional measurement methods requiring the measuring devices and the model optimization is compared during… More >

  • Open Access


    Parameter Tuned Deep Learning Based Traffic Critical Prediction Model on Remote Sensing Imaging

    Sarkar Hasan Ahmed1, Adel Al-Zebari2, Rizgar R. Zebari3, Subhi R. M. Zeebaree4,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3993-4008, 2023, DOI:10.32604/cmc.2023.037464

    Abstract Remote sensing (RS) presents laser scanning measurements, aerial photos, and high-resolution satellite images, which are utilized for extracting a range of traffic-related and road-related features. RS has a weakness, such as traffic fluctuations on small time scales that could distort the accuracy of predicted road and traffic features. This article introduces an Optimal Deep Learning for Traffic Critical Prediction Model on High-Resolution Remote Sensing Images (ODLTCP-HRRSI) to resolve these issues. The presented ODLTCP-HRRSI technique majorly aims to forecast the critical traffic in smart cities. To attain this, the presented ODLTCP-HRRSI model performs two major processes. At the initial stage, the… More >

  • Open Access


    An IoT Environment Based Framework for Intelligent Intrusion Detection

    Hamza Safwan1, Zeshan Iqbal1, Rashid Amin1, Muhammad Attique Khan2, Majed Alhaisoni3, Abdullah Alqahtani4, Ye Jin Kim5, Byoungchol Chang6,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2365-2381, 2023, DOI:10.32604/cmc.2023.033896

    Abstract Software-defined networking (SDN) represents a paradigm shift in network traffic management. It distinguishes between the data and control planes. APIs are then used to communicate between these planes. The controller is central to the management of an SDN network and is subject to security concerns. This research shows how a deep learning algorithm can detect intrusions in SDN-based IoT networks. Overfitting, low accuracy, and efficient feature selection is all discussed. We propose a hybrid machine learning-based approach based on Random Forest and Long Short-Term Memory (LSTM). In this study, a new dataset based specifically on Software Defined Networks is used… More >

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