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Design and optimization of triangular microstrip patch antenna using extreme learning machine (ELM)-based improved crystal structure algorithm (crystal) for C-band application

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2024年第7期37卷 e5721-e5721页

作者： Kumar, Jakkuluri Vijaya Shaby, S. Maflin Sathyabama Inst Sci & Technol Sch Elect & Elect Chennai Tamilnadu India

This article introduces crystal structure algorithm (crystal), a novel metaheuristic optimization algorithm, for fine-tuning microstrip patch antennas (MPA) in C-band applications, which utilizes crystal with dielectric constant and resonant frequency inputs to optimize MPA dimensions, addressing limited bandwidth and gain issues. Achieves superior antenna performance with 32.71 dB return loss, 5.09 dB gain, and 1.2 VSWR, demonstrating improved convergence speed, radiation pattern, and suitability for diverse C-band conditions. The crystal structure algorithm (crystal) is utilized to improve the geometric dimensions of the microstrip antennas for C-band application. The extreme learning algorithm is used to calculate the fitness value of the introduced crystal. This design of antenna is optimized to specific frequency bands and applications by ensuring optimal performance in diverse wireless communication or sensing systems. image

关键词： crystal structure algorithm extreme learning machine microstrip patch antenna triangular microstrip patch antenna

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Random decision forest (RDF) and crystal structure algorithm (crystal) for uncertainty consideration of RES & load demands with optimal design of hybrid CCHP systems

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ENERGY 2023年第1期282卷

作者： Gopila, M. Suresh, G. Prasad, D. Sona Coll Technol Dept Elect & Elect Engn Salem 636005 Tamil Nadu India

This manuscript proposes a multi-objective random development mode of hybrid Combined Cooling, Heating, And Power (CCHP) system, supply configuration, enclosing a turbine, cooler/heater, battery and storage tank, and photovoltaic/thermal collectors. The proposed optimal strategy is consolidation of Random Decision Forest (RDF) and crystal structure algorithm (crystal), hence it is called RDF-crystal technique. The Power center models of power converters along with storage devices are create by assuming the characteristics of non-design elements. The annual value rate falls when system confidence levels drop and uncertainty is used. The yearly value savings rate is shown to be highly sensitive to the price of fossil-fuels as a result of the sensitivity analysis of economic-frontiers based on important economic factors. As a result, the inversion of star-collectors includes a robust impact of the turbine.

关键词： Photovoltaic Combined cooling Heating and power system crystal structure algorithm Gas turbine Random decision forest

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A crystal-RDF technique-based integrated circuit topology for fast charging station of electric vehicle (EV)

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ELECTRICAL ENGINEERING 2024年第1期106卷 741-754页

作者： Ravindran, Mohammed Abdullah Nallathambi, Kalaiarasi SRM Inst Sci & Technol Dept Elect & Elect Engn Chennai Tamil Nadu India

The range of electric vehicles (EVs) is still limited due to the long amount of time it takes to charge them. However, to overcome the time constraint of recharging electric vehicle batteries, fast charging stations (FCS) can be installed. These stations are capable of fully charging a vehicle's battery in just a few minutes. For this purpose, this manuscript proposes a unidirectional boost converter and Swiss rectifier-based topology to develop an FCS for electric vehicles by using a hybrid control technique. The proposed control method is a combination of both a crystal structure algorithm (crystal) and a random decision forest (RDF). Hence, it is called the crystal-RDF method. Here, the unidirectional boost converter is utilized to enhance the power factor (PF) and also mitigate harmonics. The voltage of direct current (DC) is controlled at the output side when an unwanted perturbation is found at the AC end. The proposed rectifier is utilized to achieve better efficiency. The objective of the proposed approach is to create a fast charging station that can refill the battery of an electric vehicle quickly and efficiently and reduce the total harmonic distortion (THD). Also, in this study, the current, voltage, and duty cycle are considered initial parameters. The crystal technique is used to generate a control signal, which is given to the RDF technique. The optimal control signal is predicted by changing the duty cycle using the RDF technique. The proposed charging station includes an intermediate storage battery, which is utilized to mitigate power pulsations in the grid and to offer extra functionality. At last, the proposed method is simulated in MATLAB, and the performance is analysed with existing methods. The existing approaches, such as PSO, ALO, and SSA, and the proposed method become 4, 6.5, 2.4, and 1.7%, respectively. From this analysis, it concludes that the proposed method has lower THD compared with existing methods.

关键词： crystal structure algorithm Electric vehicle Fast charging station Random decision forest Unidirectional boost converter

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Assessment of ultimate bearing capacity of rock-socketed piles using hybrid approaches

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第4期7卷 3673-3694页

作者： You, Rongjun Mao, Huijun Yinchuan Univ Sci & Technol Sch Informat Engn Yinchuan 750011 Ningxia Peoples R China

In geotechnical engineering, accurately estimating the ultimate bearing capacity (Qu\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${Q}_{u}$$\end{document}) of rock-socketed piles remains a crucial challenge. This study introduces a methodology that integrates advanced optimization algorithms, specifically Dandelion Optimization (DO) and the crystal structure algorithm (crystal), with Support Vector Regression (SVR) to enhance predictive capabilities. Three distinct models-SVDO, SVCS, and a hybrid SVR model-are developed through this integration. The core of this predictive framework is SVR, known for its efficacy in capturing intricate non-linear relationships between input variables and the ultimate bearing capacity of rock-socketed piles. To improve predictive accuracy, DO strategically adjusts hyperparameters to emulate the growth and dispersal patterns of dandelion seeds, while crystal delicately optimizes SVR parameters inspired by crystalline atomic structures. The resulting models offer valuable insights for precisely predicting the ultimate bearing capacity of rock-socketed piles in geotechnical engineering. Among these, SVCS stands out with an exceptional R2 value of 0.997, indicating an outstanding fit to the data, and the lowest Root Mean Squared Error (RMSE) at 930.7, underscoring its unparalleled predictive accuracy. In conclusion, this study presents an innovative approach within geotechnical engineering for the precise estimation of the ultimate bearing capacity of rock-socketed piles. The insights gained contribute significantly to considerations of stability and safety in construction projects, emphasizing a multidisciplinary approach beyond artificial intelligence.

关键词： Ultimate bearing capacity Support Vector Regression Dandelion Optimization crystal structure algorithm

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Optimizing Student Performance Prediction: A Data Mining Approach with MLPC Model and Metaheuristic algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第4期15卷 55-71页

作者： Hai, Qing Wang, Changshou Hetao Coll Dept Water Resources & Civil Engn Bayan Nur 01500 Inner Mongolia Peoples R China Hetao Coll Dept Agr Bayan Nur 01500 Inner Mongolia Peoples R China

Given the information stored in educational databases, automated achievement of the learner's prediction is essential. The field of educational data mining (EDM) is handling this task. EDM creates techniques for locating data gathered from educational settings. These techniques are applied to comprehend students and the environment in which they learn. Institutions of higher learning are frequently interested in finding how many students will pass or fail required courses. Prior research has shown that many researchers focus only on selecting the right algorithm for classification, ignoring issues that arise throughout the data mining stage, such as classification error, class imbalance, and high dimensionality data, among other issues. These kinds of issues decreased the model's accuracy. This study emphasizes the application of the Multilayer Perceptron Classification (MLPC) for supervised learning to predict student performance, with various popular classification methods being employed in this field. Furthermore, an ensemble technique is utilized to enhance the accuracy of the classifier. The goal of the collaborative approach is to address forecasting and categorization issues. This study demonstrates how crucial it is to do algorithm fine-tuning activities and data pretreatment to address the quality of data concerns. The exploratory dataset utilized in this study comes from the Pelican Optimization algorithm (POA) and crystal structure algorithm (CSA). In this research, a hybrid approach is embraced, integrating the mentioned optimizers to facilitate the development of MLPO and MLCS. Based on the findings, MLPO2 demonstrated superior efficiency compared to the other methods, achieving an impressive 95.78% success rate.

关键词： Educational data mining multilayer perceptron classification pelican optimization algorithm crystal structure algorithm student performance

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Investigating the estimation of optimum moisture content through support vector regression in individual and hybrid approaches

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第4期7卷 3391-3403页

作者： Yao, Jinle Zhang, Ying Liang, Xiao Ding, Tianyi Zhejiang Normal Univ Sch Math Sci Jinhua 321004 Zhejiang Peoples R China Jiangnan Univ Sch Mech Engn Wuxi 214122 Jiangsu Peoples R China

This study explores the application of machine learning (ML) for predicting the optimum moisture content (OMC) of soil-stabilizer mixtures. The study utilizes support vector regression (SVR), a well-established ML technique, to develop comprehensive and precise models that establish a relationship between the OMC of various properties of natural soil and stabilized soil, such as particle-size linear shrinkage, plasticity, distribution, and the kind and amount of chemicals used to stabilize. To evaluate the sensitivity of OMC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{OMC}}$$\end{document} to variations in influential factors, a diverse dataset comprising different soil types and previously published stabilization testing results is employed. Additionally, the study incorporates two meta-heuristic algorithms, namely artificial rabbits optimization (ARO) and crystal structure algorithm (CSA), to improve the accuracy of the models further. These algorithms are used to validate the models by analysing OMC samples from various soil types obtained from previous stabilization test results. The findings of the study revealed three distinct models: hybrid forms of SVCS (SVR + CSA), SVAR (SVR + ARO), and an individual SVR model. Each of these models provides valuable insights that contribute to the accurate prediction of OMC for soil-stabilizer mixtures. The SVCS model demonstrates outstanding performance, evidenced by an impressive R2 value of 0.9759\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.9759$$\end{document} and an exceptionally low RMSE value of 1.184%\documentclass[12pt]{minimal} \usepackage{am

关键词： Optimum moisture content Support vector regression Artificial rabbits optimization crystal structure algorithm

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Evaluating the Impact of Yoga Practices to Improve Chronic Venous Insufficiency Symptoms: A Classification by Gaussian Process

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第8期15卷 829-840页

作者： Gou, Feng Yun Inner Mongolia Univ Technol Hohhot 010051 Inner Mongolia Peoples R China

Chronic Venous Insufficiency (CVI) is a widespread condition marked by diverse venous system irregularities stemming from occlusion and varicosities. Factors like family history and lifestyle choices amplify CVI's economic consequences, emphasizing the need for proactive measures. The sedentary lifestyle of many individuals can contribute to various diseases, including CVI. Yoga is now endorsed as a multifaceted exercise to alleviate CVI symptoms, offering a holistic approach and complementary therapy for diverse medical conditions. This study developed a method for evaluating and classifying symptoms associated with varicose veins, utilizing the Venous Clinical Score (VCSS) data. A specific emphasis was placed on investigating the impact of yoga on these symptoms, and a comprehensive performance assessment was conducted based on data obtained from a cohort of 100 patients. This paper achieves optimal performance by employing the Gaussian Process Classifier (GPC) along with two optimizers, namely the crystal structure algorithm (CSA) and the Fire Hawk Optimizer (FHO). The results indicate that in predicting VCSS-Pre (reflecting symptoms before engaging in yoga exercises), the GPFH exhibited superior performance with an F1-score of 0.872, surpassing the GPCS, which attained an F1-score of 0.861 by almost 1.26%. Additionally, the prediction for VCSS-1, reflecting symptoms after one month of yoga practices, revealed the GPFH outperforming the GPCS with respective F1-score values of 0.910 and 0.901.

关键词： Chronic venous insufficiency yoga Gaussian Process Classifier crystal structure algorithm Fire Hawk Optimizer

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Using the automated random forest approach for obtaining the compressive strength prediction of RCA

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第2期7卷 855-867页

作者： Wu, Yujie He, Xiaoming Chongqing Univ Sch Civil Engn Chongqing 400044 Peoples R China Chongqing Inst Foreign Studies Sch Int Business & Management Chongqing 401120 Peoples R China Henan Commun Planning & Design Inst Co Ltd Zhengzhou 451450 Henan Peoples R China

The intricate relationships and cohesiveness among numerous components make designing mixture proportions for high-performance concrete (HPC) challenging. Machine learning (ML) algorithms are indeed efficacious in mitigating this predicament. However, their lack of an explicit correlation between mixture proportions and compressive strength renders them opaque black-box models. To surpass this constraint, the present research puts forward a semi-empirical methodology that utilizes tactics such as non-dimensionalization and optimization. The proposed methods exhibit a remarkable accuracy in predicting compressive strength across various datasets, exemplifying its all-encompassing applicability to diverse datasets. Furthermore, the exact association furnished by semi-empirical equations is valuable for engineers and researchers in this domain, especially concerning their prognostic capabilities. The compressive strength of concrete holds significant importance in designing high-performance concrete, and achieving an optimal mixture proportion necessitates a comprehensive comprehension of the complex interplay among diverse factors, including the type and proportion of cement, water-cement ratio, size and type of aggregate, curing conditions, and admixtures. The semi-empirical approach put forth in this study presents a potential remedy to the intricate undertaking by establishing a more unequivocal correlation between mixture ratios and compressive strength.

关键词： High-performance concrete Random forest crystal structure algorithm Bonobo optimizer Sunflower optimization algorithm

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Applying Support Vector Regression-Based Hybrid Models For Modeling The Gasification Process

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2024年第6期28卷 1195-1208页

作者： Gong, Baofu Criminal Invest Police Univ China Lib Shenyang 110854 Peoples R China

Modeling the gasification process through machine learning (ML) involves predicting the behavior and performance of gasification systems. Support Vector Regression (SVR) is known as an effective procedure in forecasting continuous variables and is even suitable for the modeling gasification process. Employing optimization algorithms to connect and fine-tune internal model settings can lead to the creation of various hybrid and ensemble models. Generally, employing hybrid models has demonstrated enhanced performance while utilizing cost-effective modeling techniques. In this study, SVR was utilized as a machine learning method, alongside the crystal structure algorithm (crystal) and the Population-based Vortex Search algorithm (PVSA), to fine-tune SVR for accurately assessing CO and CO2 values. After evaluating the outcomes of the proposed models, it was observed that the SVR-PVSA hybrid model outperformed the SVR-crystal model, with differences of 1%, 19%, and 57% based on R2, RMSE, and MAE indices, respectively for CO and that of 1%, 14%, and 54% for CO2 in terms of R2, RMSE, and MAE evaluators, respectively. Furthermore, for predicting both CO and CO2, the SVR-crystal hybrid model yielded the highest value, demonstrating superior performance compared to the SVR-PVSA model, with an average difference of 0.6% and 0.9% in terms of the VAF index.

关键词： Gasification modeling Machine learning Support Vector Regression crystal structure algorithm Population-based Vortex Search algorithm

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USING THE AUTOMATED RANDOM FOREST APPROACH FOR OBTAINING THE COMPRESSIVE STRENGTH PREDICTION OF RCA

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CIVIL ENGINEERING JOURNAL-STAVEBNI OBZOR 2023年第4期32卷 1-14页

The intricate relationships and cohesiveness among numerous components make the task of designing mixture proportions for high-performance concrete (HPC) a challenging endeavour. Machine learning (ML) algorithms are indeed efficacious in mitigating this predicament. However, their lack of an explicit correlation between mixture proportions and compressive strength renders them opaque black box models. To surpass this constraint, the present research puts forward a semi-empirical methodology that involves the utilization of tactics such as non-dimensionalization and optimization. The methodology proposed exhibits a remarkable level of accuracy in predicting compressive strength across various datasets, exemplifying its all-encompassing applicability to diverse ***, the exact association furnished by semi-empirical equations is a valuable asset for engineers and researchers operating in this domain, especially concerning their prognostic capabilities. The compressive strength of concrete holds significant importance in designing high-performance concrete, and achieving an optimal mixture proportion necessitates a comprehensive comprehension of the complex interplay among diverse factors, including the type and proportion of cement, water-cement ratio, size and type of aggregate, curing conditions, and admixtures. The semi-empirical approach put forth in this study presents a potential remedy to the intricate undertaking by establishing a more unequivocal correlation between mixture ratios and compressive strength.

关键词： High-performance concrete Sunflower Optimization algorithm Random forest crystal structure algorithm Bonobo Optimizer

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