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Advanced Kernel function-based models and soft computing techniques for optimizing oxygen transfer efficiency in solid jet aerators with circular openings: a comparative study of Gaussian process regression, random forest and support vector machine approaches

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2025年第1期8卷 1-20页

作者： Shukla, Bishnu Kant Goel, Arun Sharma, Pushpendra Kumar Sihag, Parveen Lovely Profess Univ Sch Civil Engn Phagwara 144411 India Natl Inst Technol Dept Civil Engn Kurukshetra 136119 India Chandigarh Univ Dept Civil Engn Mohali 140413 India

This research delves into the optimization of Oxygen Transfer Efficiency (OTE) for solid jet aerators featuring circular openings by applying a range of advanced soft computing methodologies. The study considers a diverse set of operational variables, including the number of jets (1, 2, 4, 8), jet lengths (170 mm, 270 mm, 370 mm, 470 mm), and discharge rates (1.05 l/s, 1.54 l/s, 1.93 l/s, 2.46 l/s, 3.04 l/s). By integrating empirical data with soft computing techniques such as Random Forest (RF), Gaussian Process Regression (GPR), Support Vector Machine Regression (SVMR), and Non-Linear Regression (NLR), the research significantly enhances OTE predictive accuracy. Performance parameters, including coefficient of correlation (C.C), Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), scattering index (SI), and mean absolute error (MAE), were evaluated. Results indicate that Gaussian Process regression with a radial basis kernel outperforms other techniques with 1.0000 and 0.9994 (CC), 0.0364 and 0.1242 (MAE), 0.0567 and 0.2305 (RMSE), 0.0107 and 0.0390 (SI), and 0.9999 and 0.9987 (NSE) for training and testing datasets. Sensitivity analysis shows discharge significantly influences OTE, with CC value of 0.0683, MAE of 5.025, and RMSE of 6.8018. The number of openings also impacts OTE. The findings underscore the GPR_RBF model's robustness, offering valuable insights for optimizing aeration systems in wastewater treatment and related industrial applications. This study contributes to the broader field of environmental engineering by demonstrating the application of AI-driven models to address real-world complexities in aeration processes.

关键词： Oxygen transfer efficiency soft computing techniques Kernel function-based models Circular jets Gaussian process regression (GPR)

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soft computing techniques for Surface Roughness Prediction in Hard Turning: A Literature Review

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IEEE ACCESS 2019年 7卷 89556-89569页

作者： He, Kang Gao, Mengdi Zhao, Zhuanzhe Suzhou Univ Sch Mech & Elect Engn Suzhou 234000 Peoples R China Anhui Polytech Univ Sch Mech & Automot Engn Wuhu 241000 Peoples R China

Hard turning has become an attractive alternative to the more time-consuming and costly grinding technique. Unfortunately, high-quality prediction of the surface roughness generated during hard turning is difficult due to the technical complexities involved. Hence, it is currently receiving much research attention. The objective of this paper is to survey the current state of the soft computing techniques for surface roughness prediction in hard turning. It focuses on three areas: data acquisition, feature selection, and prediction model of surface roughness. First, the characteristics of hard turning and surface roughness are introduced, and a framework of the soft computing techniques is presented. Then, the three key areas are surveyed thoroughly. Finally, the recommendations and challenges faced by industry and academia are discussed, and the conclusions are drawn.

关键词： Surface roughness prediction soft computing techniques hard turning review

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soft computing techniques in advancement of structural metals

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INTERNATIONAL MATERIALS REVIEWS 2013年第8期58卷 475-504页

作者： Datta, S. Chattopadhyay, P. P. Bengal Engn & Sci Univ Sch Mat Sci & Engn Sibpur 711103 Howrah India Bengal Engn & Sci Univ Dept Met & Mat Engn Sibpur 711103 Howrah India

Current trends in the progress of technology demand availability of materials resources ahead of the advancing fronts of the application areas. During the last couple of decades, significant progress has been made in computational and experimental design of materials. Among the potential computational techniques, soft computing stands in distinction due to the inherent flexibility in capturing the complexity of the problem in global scale. Since 1990s remarkable success has been achieved in soft computing activities in different facets of materials science and engineering. Extensive efforts have been devoted in design of metals and alloys based on composition-process-microstructure-property correlation. The present review aims to address the contribution of soft computing in the field of structural metals and alloys including processing and joining. The critical issues concerning applicability of particular techniques in specific materials problem have been particularly emphasised encompassing the scope of integrating the gradual progress in different techniques in hybrid and tandem framework to address greater complexities in larger length and time scale. Attempt has also been made to emphasise on the evolution of newer knowledge and materials through soft computing activities. Finally, the potential of soft computing techniques in futuristic design approaches has been critically enumerated.

关键词： Composition-processing-structure-property correlation Structural metals and alloys soft computing techniques Design of materials

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soft computing techniques for biomedical data analysis: open issues and challenges

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第2-Sup期56卷 2599-2649页

作者： Houssein, Essam H. Hosney, Mosa E. Emam, Marwa M. Younis, Eman M. G. Ali, Abdelmgeid A. Mohamed, Waleed M. Minia Univ Fac Comp & Informat Al Minya Egypt Luxor Univ Fac Comp & Informat Luxor Egypt

In recent years, medical data analysis has become paramount in delivering accurate diagnoses for various diseases. The plethora of medical data sources, encompassing disease types, disease-related proteins, ligands for proteins, and molecular drug components, necessitates adopting effective disease analysis and diagnosis methods. soft computing techniques, including swarm algorithms and machine learning (ML) methods, have emerged as superior approaches. While ML techniques such as classification and clustering have gained prominence, feature selection methods are crucial in extracting optimal features and reducing data dimensions. This review paper presents a comprehensive overview of soft computing techniques for tackling medical data problems through classifying and analyzing medical data. The focus lies mainly on the classification of medical data resources. A detailed examination of various techniques developed for classifying numerous diseases is provided. The review encompasses an in-depth exploration of multiple ML methods designed explicitly for disease detection and classification. Additionally, the review paper offers insights into the underlying biological disease mechanisms and highlights several medical and chemical databases that facilitate research in this field. Furthermore, the review paper outlines emerging trends and identifies the key challenges in biomedical data analysis. It sheds light on this research domain's exciting possibilities and future directions. The enhanced understanding of soft computing techniques and their practical applications and limitations will contribute to advancing biomedical data analysis and support healthcare professionals in making accurate diagnoses.

关键词： soft computing techniques Biomedical data analysis Machine learning feature selection Artificial neural networks Swarm optimization algorithms

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soft computing techniques Applied to Distribution Network Reconfiguration: A Survey of the State-of-the-Art 8

Soft Computing Techniques Applied to Distribution Network Re...

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8th International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Srivastava, Ishan Bhat, S. S. VNIT Elect Engn Dept Nagpur Maharashtra India

ISBN: (纸本)9781509011445

Network reconfiguration is the process of altering the topological structure of distribution feeders to achieve more reliable and economic operation of distribution network. Distribution Network Reconfiguration (DNR) is a multi-objective, combinatorial, non-differentiable constrained optimization problem. This strategy nowadays is very frequently used by distribution engineers to meet various objectives like loss minimization and power quality enhancement. This paper describes the various possible objectives and constraints of radial network reconfiguration. This paper also presents a comprehensive review of various strategies of network reconfiguration which are mainly based on heuristic methods and intelligent optimization algorithm.

关键词： Feeder Reconfiguration Combinatorial Heuristic Methods Intelligent Optimization Algorithms soft computing techniques

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soft computing techniques for Designing of Adaptive Power System Stabilizer

Soft Computing Techniques for Designing of Adaptive Power Sy...

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IEEE Milan PowerTech Conference

作者： Gandhi, P. R. Joshi, S. K. Sardar Vallabhbhai Patel Inst Technol Dept Elect Engn Vasad India Maharaja Sayajirao Univ Baroda Dept Elect Engn Vadodara India

ISBN: (纸本)9781538647226

This paper presents the design of power system stabilizer using various computational intelligence techniques. The Genetic Algorithm based PID (GA-PID), Levenberg Marquardt neural network (LMNN), genetics algorithm based artificial neural network (GA-ANN), non-linear autoregressive moving average-L2 norm (NARMA-L2) and adaptive neural fuzzy inference system (ANFIS) techniques are used for designing of power system stabilizer (PSS) to enhance the dynamic power system stability. The simulations of SMIB have been performed with PSS, which has been designed by various artificial intelligence techniques. The capability and effectiveness of design have verified and have compared under the various disturbances as well as different operating conditions.

关键词： soft computing techniques Nonlinear Analysis PSS

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soft computing techniques in Modeling the Influence of pH on Dopamine Biosensor

Soft Computing Techniques in Modeling the Influence of pH on...

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4th International IEEE Conference Intelligent Systems

作者： Rangelova, Vania I. Tsankova, Diana D. Tech Univ Sofia Dept Elect Engn Branch Plovdiv 25 Tsanko Dyustabanov Str Plovdiv 4000 Bulgaria Techn Univ Sofia Control Syst Dept Sofia Bulgaria

ISBN: (纸本)9781424417391

Recently the soft computing techniques have become an important alternative tool to conventional methods in modeling complex non-linear relationships. Since the electrochemical biosensors use an enzyme reaction for measuring different types of substrates, the pH and temperature influence strongly on the output signal of biosensors. The paper treats soft computing modeling the input/output non-linear dependence of a dopamine biosensor, which uses an active membrane from banana tissue. The model represents the biosensor's output current versus the substrate concentration and pH. The temperature is not taken into account, it is set to be constant during the experiments. The problem to solve here is to find a way of increasing the accuracy (and the fastness) of the modeling process, under condition of insufficient experimental data. The following soft computing techniques are compared in MATLAB environment: (1) neural network with backpropagation learning algorithm, (2) CMAC neural network, (3) fuzzy logic, and (4) ANFIS. The relative errors over a few new experimental samples are calculated for validation of the proposed models.

关键词： Biosensor dopamine pH soft computing techniques

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Spatial context-based optimal multilevel energy curve thresholding for image segmentation using soft computing techniques

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NEURAL computing & APPLICATIONS 2020年第13期32卷 8901-8937页

作者： Kandhway, Pankaj Bhandari, Ashish Kumar Natl Inst Technol Patna Dept Elect & Commun Engn Patna 800005 Bihar India

Image segmentation using multilevel thresholding (MT) is one of the leading methods. Although, as most techniques are based on the image histogram to be segmented, MT approaches only include the occurrence frequency of particular intensity range disregarding each spatial information. Energy curve-based contextual information can help to improve the quality of the thresholded image as it computes not only the value of the pixel but also its vicinity. Therefore, the energy curve is intended to carry spatial information into a curve with the same properties as the histogram. In this paper, classical Otsu's method (between-class variance) is combined with energy curve for multilevel thresholding to perform segmentation of colored images. The energy curve-based Otsu (Energy-Otsu) uses an exhaustive search process to determine the optimal threshold values. However, due to the multidimensionality and multimodal nature of the color images, it becomes challenging and highly complex to obtain optimal thresholds. Therefore, the cuckoo search (CS) algorithm is coupled with Otsu thresholding criteria to perform MT over the energy curve. The proposed Energy-Otsu-CS method produces better-segmented results as compared to other well-known optimization algorithms such as differential evolution, particle swarm optimization, firefly algorithm, bacterial foraging optimization, and artificial bee colony algorithm. The proposed approach is examined intensively regarding quality, and the numerical parameter analysis is presented to compare the segmented results of the algorithms against closely related current approaches such as gray-level co-occurrence matrix and Renyi' entropy-based thresholding approaches.

关键词： Multilevel thresholding Renyi's entropy Spatial context sensitive Energy-Otsu method Gray-level co-occurrence matrix soft computing techniques

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Multi-objective exergetic and technical optimization of a piezoelectric ultrasonic reactor applied to synthesize biodiesel from waste cooking oil (WCO) using soft computing techniques

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FUEL 2019年 235卷 100-112页

作者： Aghbashlo, Mortaza Hosseinpour, Soleiman Tabatabaei, Meisam Soufiyan, Mohamad Mojarab Univ Tehran Coll Agr & Nat Resources Fac Agr Engn & Technol Dept Mech Engn Agr Machinery Karaj Iran ABRII Microbial Biotechnol & Biosafety Dept POB 31535-1897 Karaj Iran BRTeam Karaj Iran

This work was devoted to optimizing the operating conditions of a low-energy consumption, high frequency piezoelectric ultrasonic reactor used for biodiesel production from waste cooking oil (WCO). The optimization was conducted on the basis of exergetic and technical constraints using a consolidated combination of soft computing techniques. Transesterification temperature, residence time, methanol/oil molar ratio were input variables, while functional exergy efficiency (FEE), universal exergy efficiency (UEE), normalized exergy destruction (NED), and conversion efficiency (CE) were considered as output parameters. Four independent ANFIS models were first developed to map the outputs as a function of the inputs. The developed ANFIS models were then introduced to a hybridized optimization paradigm obtained by consolidating the elitist non-dominated sorting genetic algorithm (NSGA-II) and linear interdependent fuzzy multi-objective optimization (ALIFMO) approaches. The optimization was set to maximize the FEE and UEE and to minimize the NED, while satisfying the ASTM standard on CE (i.e., biodiesel content > 96.5%). All the developed ANFIS models excellently estimated the outputs with an R-2 approximate to 1.0. The transesterification temperature of 60 degrees C, residence time of 10 min, and methanol/oil molar ratio of 6.20 were selected as the best operating conditions among the spectrum of the solutions suggested by the developed approach.

关键词： Biodiesel production Exergy analysis Multi-objective optimization Piezoelectric ultrasonic reactor soft computing techniques Waste cooking oil

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An evolutionary gray gradient algorithm for multilevel thresholding of brain MR images using soft computing techniques

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APPLIED soft computing 2017年 50卷 94-108页

作者： Panda, Rutuparna Agrawal, Sanjay Samantaray, Leena Abraham, Ajith VSS Univ Technol Dept Elect & Telecommun Engn Burla 768018 Odisha India ABIT Dept Elect & Commun Engn Cuttack Odisha India Sci Network Innovat & Res Excellence MIR Labs Auburn WA 98071 USA

The conventional two dimensional (2-D) histogram based Otsu's method gives unreliable results while considering multilevel thresholding of brain magnetic resonance (MR) images, because the edges of the brain regions are not preserved due to the local averaging process involved. Moreover, some of the useful pixels present inside the off-diagonal regions are ignored in the calculation. This article presents an evolutionary gray gradient algorithm (EGGA) for optimal multilevel thresholding of brain MR images. In this paper, more edge information is preserved by computing 2-D histogram based gray gradient. The key to our success is the use of the gray gradient information between the pixel values and the pixel average values to minimize the information loss. In addition, the speed improvement is achieved. Theoretical formulations are derived for computing the maximum between class variance from the 2-D histogram of the brain image. A first-hand fitness function is suggested for the EGGA. A novel adaptive swallow swarm optimization (ASSO) algorithm is introduced to optimize the fitness function. The performance of ASSO is validated using twenty three standard Benchmark test functions. The performance of ASSO is better than swallow swarm optimization (SSO). The optimum threshold value is obtained by maximizing the between class variance using ASSO. Our method is tested using the standard axial T2 weighted brain MRI database of Harvard medical education using 100 slices. Performance of our method is compared to the Otsu's method based on the one dimensional (1-D) and the 2-D histogram. The results are also compared among four different soft computing techniques. It is observed that results obtained using our method is better than the other methods, both qualitatively and quantitatively. Benefits of our method are - (i) the EGGA exhibits better objective function values;(ii) the EGGA provides us significantly improved results;and (iii) more computational speed is achieved.

关键词： Multilevel thresholding MRI brain soft computing techniques Otsu's method

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