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Parkinson's disease and cleft lip and palate of pathological speech diagnosis using deep convolutional neural networks evolved by IPWOA

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APPLIED ACOUSTICS 2022年 199卷

作者： Yao, Dengfeng Chi, Wanle Khishe, Mohammad Beijing Union Univ Beijing Key Lab Informat Serv Engn Beijing 100101 Peoples R China Tsinghua Univ Sch Humanities Lab Computat Linguist Beijing 100084 Peoples R China Wenzhou Polytech Coll Artificial Intelligence Wenzhou 325035 Zhejiang Peoples R China Tech Univ Malaysia Malacca Fac Informat & Commun Technol Malacca 76100 Malacca State Malaysia Imam Khomeini Marine Sci Univ Dept Elect Engn Nowshahr Iran

A high-level abstract idea of speech is created in previously autonomous systems using unsupervised models. Given that these representations are typically acquired by input reconstruction, it cannot be said with assurance that they are resistant to cues unrelated to disease. Pathology diagnosis cannot usually be reliably performed using unsupervised representations. As a result, in this research, we use pathological voice recognition using deep convolutional neural networks (DCNNs). Even though DCNNs have many acknowledged benefits, selecting the best structure for them can be challenging. This work examines the use of the whale optimization algorithm (WOA) to automatically choose the best architecture for DCNNs in an effort to address this constraint. In order to achieve the goal, three canonical WOA-based innovations are suggested. First, a special encoding technique based on Internet Protocol Addresses (IPA) is created to make it easier to encode the DCNN layers with whale vectors. The development of variable-length DCNNs is then suggested using an enfeebled layer that has particular whale vector dimensions. The final step in the learning process involves splitting huge datasets into smaller ones and then randomly reviewing them. Pathological audio signals captured from patients are used to assess the performance of the proposed model. In this regard, five measures were used to conduct thorough research, including ROC and precision-recall curves, F1-Score, sensitivity, specificity, accuracy, and precision. Up to 95.77 percent of the two disordered speech signals are correctly classified by the suggested model, which outperforms the second-best algorithm, VLNSGA-II, by 1.02 percent in terms of accuracy. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： Deep CNNs Pathological speech Parkinson's disease whale optimization algorithm Cleft lip and palate

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IRI: An intelligent resistivity inversion framework based on fuzzy wavelet neural network

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 202卷 1页

作者： Dong, Li Jiang, Feibo Li, Xiaolong Wu, Mingzhu Hunan Univ Technol & Business Changsha Social Lab Artificial Intelligence Changsha Peoples R China Hunan Normal Univ Hunan Prov Key Lab Intelligent Comp & Language In Changsha Peoples R China

To acquire high-quality resistivity inversion results, an intelligent resistivity inversion (IRI) framework based on a fuzzy wavelet neural network (FWNN) is proposed in this paper. In the IRI framework, first, the FWNN is applied to build an interpretable inversion model for analyzing the apparent resistivity data. Takagi-Sugeno-Kang (TSK) fuzzy model is introduced to FWNN to explain the rules of the inversion results, and the wavelet neural network (WNN) is applied to construct the consequent part for each fuzzy rule and enhance the high-order nonlinear fitting ability of TSK fuzzy model. Then, to enhance the generalization and robustness, an elastic gradient descent (EGD) method is designed, which is used to update the linear parameters of the FWNN. Next, a differential and adaptive whale optimization algorithm (DAWOA) is introduced to train the nonlinear parameters of the FWNN for avoiding local optimum. Moreover, in the proposed DAWOA, a differential foraging strategy and an adaptive predation strategy are introduced to improve global exploration and local exploitation. All these measures can improve the inversion accuracy and accelerate the training process of FWNN for ERI inversion. Several simulation results demonstrate the feasibility and applicability of the IRI framework.

关键词： Electrical resistivity imaging Fuzzy wavelet neural network whale optimization algorithm Elastic net

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Optimizing fracture parameters in order to select based on theoretical concepts and concrete fracture energy prediction

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ENGINEERING FAILURE ANALYSIS 2024年 158卷

作者： Mirhosseini, Ramin Tabatabaei Aflatoonian, Moein Islamic Azad Univ Fac Engn Dept Civil Engn Kerman Branch Kerman Iran

Fracture energy is a critical factor in modeling the fracture behavior of quasi-brittle materials like concrete, a long-standing interest in the safety design of structures. Concrete exhibits sophisticated non-linear behavior during fracture and crack growth. The prediction accuracy of fracture energy using existing equations has always been of extensive interest to researchers. The current study has optimized the coefficients of a series of well-known approximate models using the whale optimization algorithm. This optimization allows for more accurate predictions of concrete's fracture energy, taking into account fracture parameters such as compressive strength, maximum aggregate size, and water-to-cement ratio. Compressive strength, maximum aggregate size, water-to-cement ratio, and aggregate type (river or crushed) were extracted and tabulated from reliable literature, forming a comprehensive database. An approximate average prediction formula was compiled from this data. Using 70% of the specimens from this database (374 samples) and the whale algorithm, the constant coefficients of models from previous studies were optimized. This optimization minimizes the difference between the laboratory-measured fracture energy and the model predictions. As a result, these optimized models offer a more reliable tool for estimating the fracture mechanism in concrete structures.

关键词： Fracture energy Fracture parameters Optimizing Dispersion Uncertainty Prediction Standards whale optimization algorithm

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Intelligent damage diagnosis method for offshore platforms based on enhanced stabilization diagrams and convolutional neural network

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第2期35卷 026103-026103页

作者： Leng, Jiancheng Feng, Huiyu Sun, Silei Zhao, Haifeng Zhou, Guoqiang Northeast Petr Univ Sch Mech Sci & Engn Daqing 163318 Peoples R China

Considering that it is difficult to evaluate the damage state of offshore platform structures under environmental excitation by stochastic subspace identification (SSI) stability diagrams alone, an intelligent damage diagnosis method based on enhanced stability diagrams and convolutional neural network (CNN) is proposed. The data-driven SSI algorithm and covariance-driven SSI algorithm are utilized to identify stability diagrams of monitoring data, and the stability diagrams of the two algorithms are superimposed together for image enhancement. Further, the enhanced stability diagrams are used as input samples for CNN training to distinguish the damage state of the structure. In the meanwhile, the whale optimization algorithm is employed to optimize the hyper parameters of CNN to ulteriorly improve the recognition performance. The final test accuracy of CNN is 97.20%, and is 13.09% higher than before hyper parameter optimization, which indicates that the damage diagnosis method based on enhanced stability diagrams and CNN is reasonable and effective, and is expected to be applied to real-time damage diagnosis of offshore platform structures.

关键词： damage diagnosis stabilization diagram enhancement convolutional neural network stochastic subspace identification whale optimization algorithm

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Novel hybrid WOA-GBM model for patch loading resistance prediction of longitudinally stiffened steel plate girders

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THIN-WALLED STRUCTURES 2022年 177卷

作者： Tran, Viet-Linh Nguyen, Duy-Duan Vinh Univ Dept Civil Engn Vinh 461010 Vietnam

In steel plate girders (SPGs), a patch loading usually causes a local failure in the vicinity of the loading area of the girder web. However, estimating the patch loading resistance (PLR) of SPGs is challenging due to the complexity of the problem. This paper aims to develop a novel hybrid WOA-GBM model based on a whale optimization algorithm (WOA) and a gradient boosting machine (GBM) for predicting the PLR of longitudinally SPGs. Firstly, 137 tests of longitudinally stiffened SPGs subjected to patch loading are carefully collected and divided into training and test sets. Then, the most critical parameters of the GBM model are determined using 10-fold cross-validation integrated with the WOA. The results obtained from the WOA-GBM model are compared with those from adaptive boosting (AdaBoost) and extreme gradient boosting (XGBoost) models. The results show that the WOA-GBM model outperforms other models. Additionally, SHapley Additive exPlanation (SHAP) method is used to explain the prediction of the proposed WOA-GBM model globally and locally. Finally, an efficient graphical user interface (GUI) tool and a web application (WA) are developed to apply the proposed WOA-GBM model for practical use.

关键词： Adaptive boosting Extreme gradient boosting Gradient boosting machine Patch loading resistance Steel plate girder whale optimization algorithm

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Aspect-based sentiment classification model employing whale-optimized adaptive neural network

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BULLETIN OF THE POLISH ACADEMY OF SCIENCES-TECHNICAL SCIENCES 2021年第3期69卷

作者： Balaganesh, Nallathambi Muneeswaran, K. Mepco Schlenk Engn Coll Autonomous Dept Comp Sci & Engn Sivakasi Tamil Nadu India

Nowadays in e-commerce applications, aspect-based sentiment analysis has become vital, and every consumer started focusing on various aspects of the product before making the purchasing decision on online portals like Amazon, Walmart, Alibaba, etc. Hence, the enhancement of sentiment classification considering every aspect of products and services is in the limelight. In this proposed research, an aspect-based sentiment classification model has been developed employing sentiment whale-optimized adaptive neural network (SWOANN) for classifying the sentiment for key aspects of products and services. The accuracy of sentiment classification of the product and services has been improved by the optimal selection of weights of neurons in the proposed model. The promising results are obtained by analyzing the mobile phone review dataset when compared with other existing sentiment classification approaches such as support vector machine (SVM) and artificial neural network (ANN). The proposed work uses key features such as the positive opinion score, negative opinion score, and term frequency-inverse document frequency (TF-IDF) for representing each aspect of products and services, which further improves the overall effectiveness of the classifier. The proposed model can be compatible with any sentiment classification problem of products and services.

关键词： aspect-based sentiment analysis whale optimization algorithm artificial neural network opinion mining

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End-Point Dynamic Control Model for 260 Tons BOF Steelmaking Based on PWWTSVR and LWOA

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STEEL RESEARCH INTERNATIONAL 2023年第7期94卷

作者： Wang, Miao Li, Shengli Gao, Chuang Ai, Xingang Zhai, Baopeng Univ Sci & Technol Liaoning Sch Mat & Met Anshan 114051 Liaoning Peoples R China Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Liaoning Peoples R China Ansteel Informat Ind Co Ltd Automat Div Smart Steelmaking Business Div Anshan 114000 Liaoning Peoples R China

The control of end-point carbon content and temperature is very important for basic oxygen furnace (BOF) steelmaking. Herein, to improve the control precision of the BOF end-point, a new dynamic control model of the BOF end-point is proposed. First, based on the samples of low-carbon steel collected from the steel plant, a prediction model of BOF end point is established by using projection wavelet weighted twin support vector regression (PWWTSVR). It is indicated in the simulation results that the error bound of carbon content and temperature are 0.005 wt% and 10 degrees C, respectively, and the hit rates of the prediction models are 92% and 90%. Moreover, a double hit rate of 84% is higher than the other five prediction models. Based on the PWWTSVR prediction model, a Levy-flying whale optimization algorithm (LWOA) is used to optimize the objective function to establish the control model. The control model shows that the mean absolute error of the end-blow oxygen volume calculated by the model is 75.0201 Nm(3), which is smaller by comparing the other two control models. The proposed control model can provide efficient guidance for on-site smelting personnel.

关键词： basic oxygen furnaces end-point model twin support vector regression whale optimization algorithm

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Short-Term Forecasting and Uncertainty Analysis of Wind Power

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JOURNAL OF SOLAR ENERGY ENGINEERING-TRANSACTIONS OF THE ASME 2021年第5期143卷 054503-054503页

作者： Bo, Gu Keke, Luo Hongtao, Zhang Jinhua, Zhang Hui, Huang North China Univ Water Resources & Elect Power Zhengzhou 450045 Peoples R China

Accurate forecasting is the key factor in promoting wind power consumption and improving the stable operation of power systems. A short-term wind power forecasting (WPF) and uncertainty analysis method based on whale optimization algorithm (WOA), least squares support vector machine (LSSVM), and nonparametric kernel density estimation (NPKDE) was proposed in this paper. The advantages of WOA (fast convergence speed and high convergence accuracy) were used to optimize the penalty factor and kernel function width of the LSSVM model, and the calculation speed and forecasting accuracy of the LSSVM model were improved. The training sample set is classified according to the wind speed interval, and the WOA-LSSVM forecasting model is trained by subclass after classification to further improve the accuracy of short-term WPF. The NPKDE method is used to accurately calculate the probability density distribution characteristics of the forecasting error of wind power, and the confidence interval of the WPF is accurately calculated based on the probability density distribution characteristics. The calculation results show that the forecasting accuracy of the WOA-LSSVM model is higher than those of the LSSVM, long short-term memory (LSTM), and particle swarm optimization and least squares support vector machine (PSO-LSSVM) models, and the forecasting accuracy of the WOA-LSSVM model can be further improved after classifying the training sample set. The coverage of the confidence intervals in different time scales is higher than the corresponding confidence level, indicating that the NPKDE method can accurately describe the probability density distribution characteristics of the WPF errors.

关键词： whale optimization algorithm least squares support vector machine model short-term wind power forecasting nonparametric kernel density estimation confidence interval probability density distribution renewable wind turbine

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Study and development of hybrid and ensemble forecasting models for air quality index forecasting

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EXPERT SYSTEMS 2023年第10期40卷 e13449-e13449页

作者： Pradhan, Sushree Subhaprada Panigrahi, Sibarama Purohit, Sourav Kumar Dash, Jatindra Kumar Sambalpur Univ Inst Informat Technol Dept Comp Sci Engn & Applicat Sambalpur India Natl Inst Technol Dept Comp Sci & Engn Rourkela Rourkela India SRM Univ Dept Comp Sci & Engn Amaravati 522502 Andhra Pradesh India Natl Inst Technol Dept Comp Sci & Engn Rourkela 769008 Odisha India

In this paper, a viable, robust, and highly accurate additive hybrid model employing autoregressive fractionally integrated moving average (ARFIMA) and support vector machine (SVM) with functionally expanded inputs (Additive-ARFIMA-SVM) is presented for forecasting the air quality index (AQI). Additionally, thirteen additive and multiplicative hybrid models are introduced. Several alternatives in feature engineering employing functional expansion of inputs are incorporated to boost the performance of hybrid models. Furthermore, a gradient whale optimization algorithm with group best leader strategy (GWOA-GBL) based meta-heuristic algorithm is proposed. The missing values are imputed and a variable weight ensemble forecasting model is developed using the proposed GWOA-GBL algorithm. To evaluate the effectiveness of the proposed Additive-ARFIMA-SVM forecasting model with functionally expanded inputs, comparisons are made with sixteen machine learning models, including long short-term memory (LSTM), five statistical models, seventeen hybrid models, and ten variable weight ensemble models. Extensive statistical analyses are carried out on the obtained results considering four accuracy measures that show the statistical supremacy of the proposed Additive-ARFIMA-SVM model and GWOA-GBL algorithm in predicting the AQI time series. The proposed Additive-ARFIMA-SVM model with functionally expanded inputs improves the AQI forecasting performance by 16.34% than autoregressive integrated moving average, 14.47% than ARFIMA, 33.96% than XGBoost, 43.47% than SVM, 49.39% than LSTM, 8.64% than Multiplicative-ARIMA-SVM model considering symmetric mean absolute percentage error. The proposed Additive-ARFIMA-SVM model is so efficient and reliable that it can be applied to forecast other time series like stock price, electricity load, crude oil price, sunspot number, stream flow, flood, drought etc.

关键词： air quality index ARFIMA hybrid model support vector machine whale optimization algorithm

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On the design of planar antenna using Fibonacci word fractal geometry in support of public safety

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INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING 2019年第2期29卷 e21554-e21554页

作者： Singh, Gurmeet Singh, A. P. St Longowal Inst Engn & Technol Dept Elect & Commun Engn Sangrur 148106 Punjab India

In this article, a planar antenna based on Fibonacci word fractal geometry with defected ground structure (DGS) has been investigated. A novel type of antenna radiator is proposed using Fibonacci word fractal which works for public safety and dedicated short range public safety applications. Size of proposed antenna is 50x44mm and it resonates at 4.9 and 5.9 GHz having bandwidth ranging from 4.8 to 5.1 GHz and 5.8 to 6.8 GHz, respectively. Defected Ground Structure is used to improve gain for lower frequency band. Representative results of fractal patch antenna are reported to access the effectiveness of the developed approach in public safety applications. Simulation and experimental result confirms the efficiency of whale optimization algorithm for antenna design. Experimentally measured results are matches well with simulated results and quite promising.

关键词： antenna Defected Ground Structure Fibonacci Word Fractal gain public Safety whale optimization algorithm

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