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Development of a radial basis neural network for the prediction of the compressive strength of high-performance concrete

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第1期7卷 109-122页

作者： Zhang, HuiPing Gu, XiaoYong Zhang, FengJian Zhang, LiMing Henan Univ Urban Construct Sch Civil & Transportat Engn Pingdingshan 467036 Peoples R China

Concrete is a crucial component in construction projects, and engineers have been striving to improve its performance. Estimating the compressive strength, a critical feature of concrete has been a challenging task. To overcome the limitations of time-consuming experimental approaches, smart soft-based methods have been adopted due to their cost-effectiveness. In this research, different concrete mix designs that contained fly ash and micro-silica were employed to construct prediction models. To improve the accuracy of predicting the influence of admixtures on concrete's compressive strength, a novel approach was used, which included augmenting a radial basis function neural network employing equilibrium and grasshopper optimization methods. Metaheuristic algorithms were utilized to enhance the main prediction model's settings and improve accuracy. Hybrid models, namely the equilibrium-based radial-based neural network (ERBFNN) and grasshopper-based radial-based neural network (GRBFNN), were developed to estimate compressive strength. The results of the hybrid model in the training and validation phases showed that the ERBFNN model, with R-2 and RMSE values of 0.9692 and 2.5629, outperformed other models and can be considered a reliable model for compressive strength estimation.

关键词： Compressive strength Equilibrium optimization algorithm grasshopper optimization algorithm High-performance concrete Radial basis function

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Segmentation and classification of brain tumors using fuzzy 3D highlighting and machine learning

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JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY 2023年第11期149卷 9025-9041页

作者： Mowlani, Khalil Jafari Shahbazzadeh, Mehdi Hashemipour, Maliheh Islamic Azad Univ Dept Comp Engn Kerman Branch Kerman Iran Islamic Azad Univ Dept Elect Engn Kerman Branch Kerman Iran

PurposeBrain tumors are among the most lethal forms of cancer, so early diagnosis is crucial. As a result of machine learning algorithms, radiologists can now make accurate diagnoses of tumors without resorting to invasive procedures. There are, however, a number of obstacles to overcome. To begin, classifying brain tumors presents a significant difficulty in developing the most effective deep learning framework. Furthermore, physically dividing the brain tumor is a time-consuming and challenging process that requires the expertise of medical ***, we have discussed the use of a fuzzy 3D highlighting method for the segmentation of brain tumors and the selection of suspect tumor areas based on the geometric characteristics of MRI scans. After features were extracted from the brain tumor section, the images were classified using two machine learning methods: a support vector machine technique optimized with the grasshopper optimization algorithm (GOA-SVM), and a deep neural network technique based on features selected with the genetic algorithm (GA-DNN). This classifies brain tumors into benign and malignant. Implemented on the MATLAB platform, the proposed method is evaluated for effectiveness using performance metrics like sensitivity, accuracy, specificity, and Youden *** these results, it is clear that the proposed strategy is significantly superior to the alternatives. The average classification accuracy was determined to be 97.53%, 97.65%, for GA-DNN and GOA-SVM, *** findings may be a quick and important step to detect the presence of lesions at the same time as cancerous tumors in neurology diagnosis.

关键词： Brain tumors MRI scans Fuzzy 3D highlighting Support vector machine grasshopper optimization algorithm Deep neural network

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Intellectual heartbeats classification model for diagnosis of heart disease from ECG signal using hybrid convolutional neural network with GOA

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SN APPLIED SCIENCES 2021年第2期3卷 1-14页

作者： Tyagi, Ankita Mehra, Ritika DIT Univ Fac Sch Comp Dehradan Uttarakhand India

Automatic heart disease detection from human heartbeats is a challenging and intellectual assignment in signal processing because periodically monitoring of the heart beat arrhythmia for patient is an essential task to reduce the death rate due to cardiovascular disease (CVD). In this paper, the focus of research is to design hybrid Convolutional Neural Network (CNN) architecture by making use of grasshopper optimization algorithm (GOA) to classify different types of heart diseases from the ECG signal or human heartbeats. Convolutional Neural Network (CNN) as an artificial intelligence approach is widely used in computer vision-based medical data analysis. However, the traditional CNN cannot be used for classification of heart diseases from the ECG signal because lots of noise or irrelevant data is mixed with signal. So this study utilizes the pre-processing and selection of feature for proper heart diseases classification, where Discrete Wavelet Transform (DWT) is used for the noise reduction as well as segmentation of ECG signal and grasshopper optimization algorithm (GOA) is used for selection of R-peaks features from the extracted feature sets in terms of R-peaks and R-R intervals that help to attain better classification accuracy. For training as well as testing of projected Heartbeats Classification Model (HCM), the Standard MIT-BIH arrhythmia database is utilized with hybrid Convolutional Neural Network (CNN) architecture. The assortment of proper R-peaks and R-R intervals is a major factor and because of the deficiency of apposite pre-processing phases like noise removal, signal decomposition, smoothing and filtering, the uniqueness of extracted feature is less. The experimental outcomes show that the planned HCM is effective for detecting irregular human heartbeats via R-peaks and R-R intervals. When the proposed Heartbeats Classification Model (HCM) was verified on the database, model achieved higher efficiency than other state-of-the-art techniques for 16

关键词： Heartbeats Classification Model Discrete Wavelet Transform Convolutional Neural Network grasshopper optimization algorithm R-peaks and R-R Interval Analysis

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grasshopper algorithm Optimized Fractional Order Fuzzy PID Frequency Controller for Hybrid Power Systems

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RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2019年第6期12卷 519-531页

作者： Lal, Deepak Kumar Barisal, Ajit Kumar Veer Surendra Sai Univ Technol Dept Elect Engn Burla Odisha India Coll Engn & Technol Dept Elect Engn Bhubaneswar Odisha India

Background: Due to the increasing demand for the electrical power and limitations of conventional energy to produce electricity. Methods: Now the Microgrid (MG) system based on alternative energy sources are used to provide electrical energy to fulfill the increasing demand. The power system frequency deviates from its nominal value when the generation differs the load demand. The paper presents, Load Frequency Control (LFC) of a hybrid power structure consisting of a reheat turbine thermal unit, hydropower generation unit and Distributed Generation (DG) resources. Results: The execution of the proposed fractional order Fuzzy proportional-integral-derivative (FO Fuzzy PID) controller is explored by comparing the results with different types of controllers such as PID, fractional order PID (FOPID) and Fuzzy PID controllers. The controller parameters are optimized with a novel application of grasshopper optimization algorithm (GOA). The robustness of the proposed FO Fuzzy PID controller towards different loading, Step Load Perturbations (SLP) and random step change of wind power is tested. Further, the study is extended to an AC microgrid integrated three region thermal power systems. Conclusion: The performed time domain simulations results demonstrate the effectiveness of the proposed FO Fuzzy PID controller and show that it has better performance than that of PID, FOPID and Fuzzy PID controllers. The suggested approach is reached out to the more practical multi-region power system. Thus, the worthiness and adequacy of the proposed technique are verified effectively.

关键词： Fractional order controller fuzzy PID controller grasshopper optimization algorithm load frequency control microgrid power system multi-area system

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Support Vector Machine to Predict the Pile Settlement using Novel optimization algorithm

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GEOTECHNICAL AND GEOLOGICAL ENGINEERING 2023年第7期41卷 3861-3875页

作者： Ge, Qingyun Li, Caimei Yang, Fulian West Anhui Univ Luan Anhui Peoples R China Gates Winhere Automobile Water Pump Prod Yantai Co Yantai 712000 Shandong Peoples R China

Project Immunization, like piled construction, requires considerations that make them safe during the period of operation. Pile Settlement (PS), a vital issue in projects, has attracted many regards to avoid failure before commencing employing constructions. Several factors in appraising the pile movement can assist in understanding the future of the project in the loading stage. Many intelligent strategies to mathematically compute the pile motion are employed to simulate the PS. The present study aims to use Support vector regression (SVR) to predict the settlement of piles. In addition, to improve the accuracy of the related model, two meta-heuristic algorithms have been used, including the Arithmetic optimization algorithm (AOA) and grasshopper optimization algorithm (GOA), a hybrid format in the framework of SVR-AOA and SVR-GOA. Kuala Lumpur transportation network was chosen to investigate the pile motion according to the ground properties' condition with SVR-AOA and SVR-GOA developed frameworks. For the evaluation of each model's performance, five indices were employed. That, the values of RMSEs for SVR-AOA and SVR-GOA were obtained at 0.550 and 0.592, respectively, and MAE exhibited the values of 0.525 and 0.561 alternatively. The R-value for the SVR-AOA showed a desirable magnitude of 0.994, which is 0.10% higher than the SVR-GOA. Also, OBJ, including R, RMSE, and MAE, for SVR-GOA and SVR-AOA were computed at 0.541 and 0.586 mm, respectively. Models' results have had a similar performance to estimating the PS rate.

关键词： Pile settlement Support vector regression grasshopper optimization algorithm Arithmetic optimization algorithm RMSE

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Rolling bearing fault diagnosis using generalized refined composite multiscale sample entropy and optimized support vector machine

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MEASUREMENT 2020年第0期156卷 107574-000页

作者： Wang, Zhenya Yao, Ligang Cai, Yongwu Fuzhou Univ Sch Mech Engn & Automat Fuzhou 350116 Peoples R China

Rolling bearing fault diagnosis is an important and time sensitive task, to ensure the normal operation of rotating machinery. This paper proposes a fault diagnosis for rolling bearings, based on Generalized Refined Composite Multiscale Sample Entropy (GRCMSE), Supervised Isometric Mapping (S-Isomap) and grasshopper optimization algorithm based Support Vector Machine (GOA-SVM). First, GRCMSE is utilized to characterize the complexity of vibration signals, at different scales. Furthermore, an effective manifold learning algorithm, named S-Isomap, is applied, to compress the high-dimensional feature set into a low-dimensional space. Subsequently, GOA-SVM classifier is proposed for pattern recognition, having higher recognition accuracy than other classifiers. The performance of the proposed method has been verified by its successful application in a rolling bearing fault diagnosis experiment. Compared with the existing methods, this approach improves the classification accuracy to 100%. The produced results indicate that the proposed method can effectively detect bearing faults, maintaining high accuracy. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Generalized refined composite multiscale sample entropy Supervised isometric mapping grasshopper optimization algorithm Support vector machine Rolling bearing Fault diagnosis

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Earlier smart prediction of diabetic retinopathy from fundus image under innovative ResNet optimization maneuver

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ROBOTICA 2024年第7期42卷 2345-2366页

作者： Sunil, S. S. Vindhya, A. Shri St Thomas Coll Engn & Technol Dept Comp Sci & Engn Alappuzha India Saveetha Univ Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Comp Sci & Engn Chennai India

Diabetic retinopathy (DR) is a complication of diabetes that causes blindness and the early detection of diabetics using retinopathy images remains a challenging task. Hence, a novel, earlier smart prediction of diabetic retinopathy from fundus image under Innovative ResNet optimization is introduced to effectively detect the earlier stage of DR from Fundus image. Initially, the fundus image is scaled during preprocessing and converted into a grayscale format. As the existing studies neglect some deserving unique features that are crucial for predicting the earliest signs of DR, a novel Fractional Radon Transform with Visibility Graph is introduced for extracting the novel features such as microaneurysms count, dot and blot hemorrhages count, statistical measures, and retinal layer thickness, in which a Generalized Cosine Fractional Radon Transform is used to capture the image's fine-scale texture information thereby effectively capturing the statistical measures, while a weighted Horizontal Visibility Graph is made to examine the apparent spatial relationships between pixel pairs in the image based on the values of the pixels' gray levels. Further, the existing works failed to identify the small fine dark areas that were ignored throughout the morphological opening process. In order to overcome this issue, a Morphological Black Hat Transform with Optimized ResNet algorithm is implemented, where segmentation is made through Enriched Black Hat Transform-based Morphological operation to identify fine dark regions among the pixels inside the eye samples, and the classification is done by using ResNet-driven S-GOA (Socio grasshopper optimization algorithm), to optimally predict the stages of DR. The result obtained showed that the proposed model outperforms existing techniques with high performance and accuracy.

关键词： innovative ResNet diabetic retinopathy fundus image horizontal weighted visibility graph fractional radon transform grasshopper optimization algorithm

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optimization and Reliability Evaluation of Hybrid Solar-Wind Energy Systems for Remote Areas

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH 2020年第4期10卷 1696-1707页

作者： Masih, Animesh Verma, H. K. SGSITS Elect Engn Dept Indore Madhya Pradesh India

This paper presents optimization and reliability analysis of renewable energy system for farm house electricity and irrigation load demand located at remote area. Optimum sizing of solar PV, wind turbine and battery energy system has been obtained to fully utilize the capacity of the system. The economic analysis has been carried out for different configuration of hybrid energy system in terms of net present cost (NPC), operating cost (OC), levelized cost of energy (LCOE). This paper proposes a recent evolutionary technique based on meta-heuristic optimization algorithm called grasshopper optimization algorithm (GOA), to be used for selecting optimal energy system configuration. Markov method has been employed for evaluating the reliability indices i.e. system failure rate, repair rate, availability, unavailability, mean up time (MUT), mean down time (MDT), loss of load probability (LOLP) and expected energy not supplied (EENS). Economic analysis, GOA and Markov method-based result shows that hybrid PV-Wind-battery energy system is more reliable and cost effective system as compared to other configuration. The NPC and LCOE of the optimal hybrid PV-wind-battery energy system are $26,450 and 0.502$/kWh respectively. The optimal hybrid PV-wind-battery energy system reliability indices like LOLP (0.0000103), MUT (84.60), EENS (0.63189), failure rate (0.011819) and unavailability (0.09027) are evaluated.

关键词： availability grasshopper optimization algorithm hybrid energy system loss of load probability Markov model

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Employing the optimization algorithms with machine learning framework to estimate the compressive strength of ultra-high-performance concrete (UHPC)

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第1期7卷 97-108页

作者： Zhang, Yajing An, Sai Liu, Hao Hebei Normal Univ Sci & Technol Coll Urban Construction Qinhuangdao 066004 Peoples R China

Ultra-high-performance concrete (UHPC), with the highest resistance capacity against axial loads, is composed of various ingredients compared to other typical concretes, including fly ash and silica fume;eco-friendly materials also have an economical price. Such high-resistant constructional materials are seen as a suitable aggregate to use in most practical projects. The compressive strength (CS) of concrete, as one of the important variables in engineering fields, can be estimated using smart approaches based on ingredients as inputs fed to the mathematical model. Consequently, the current study modeled the CS values using a machine learning technique of Support vector regression (SVR) accompanied by the grasshopper optimization algorithm (GOA) and arithmetic optimization algorithm (AOA), tunning the SVR to appraise the CS accurately. In developing AOA-SVR and GOA-SVR frameworks, several metrics were used to assess the ability and accuracy of models. As a result of the present study, it can be concluded that the machine learning method in hybrid form has the ability to predict for saving time and energy. In general, in comparing the results of hybrid models, AOA had the most suitable combination with SVR compared to GOA, with R-2 = 0.901 and RMSE = 9.986 in the training phase. In addition, AOA-SVR improved its performance by R-2 = 0.917 and RMSE = 9.525 in the test phase.

关键词： Ultra-high-performance concrete Arithmetic optimization algorithm Support vector regression grasshopper optimization algorithm Compressive strength

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Predict the properties of self-compacting concrete using the optimized support vector regression models

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2023年第3期6卷 431-450页

作者： Li, Jie Yan, Gongxing Chen, Haojie Chongqing Creat Vocat Coll Sch Architectural Engn Chongqing 402160 Peoples R China Luzhou Vocat & Tech Coll Sch Intelligent Construct Luzhou 646000 Sichuan Peoples R China

There have been various studies on fly ash-containing concrete that focuses on foretelling the properties of hardened concrete. However, little research has been conducted to forecast the characteristics of freshly mixed and cured self-compacting concrete (SCC). In the present article, the goal was to construct a network that estimates SCC both before and after hardening character-istics using support vector regression (SVR) analysis. Developing reliable SVR models is related to determining the optimal values of key parameters, where in this article this task was done using the marine predator algorithm (MPA) and grasshopper optimization algorithm (GOA). SCC's new phase characteristics include the L-box, V-funnel, and slump flow, and cured phase characteristics include compressive strength. For all categories assessed in the training and evaluation parts, the results are highly favorable. It became clear that the presented networks had excellent R-2 values. In other words, the correlation between the actual and expected characteristics of the SCC of the hybrid system is sufficient and reflects the excellent accuracy of the development and approximation process. The MPA-SVR model outperforms GOA-SVR, demonstrating the algorithm's ability to select the most influential parameters in the approach under consideration.

关键词： Self-compacting concrete The support vector regression Marine predator algorithm grasshopper optimization algorithm

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