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An efficient algorithm for community detection in complex weighted networks

AICHE JOURNAL 2021年第7期67卷 e17205-e17205页

作者： Masooleh, Leila Samandari Arbogast, Jeffrey E. Seider, Warren D. Oktem, Ulku Soroush, Masoud Drexel Univ Dept Chem & Biol Engn Philadelphia PA 19104 USA Amer Air Liquide Newark DE USA Air Liquide China R&D Co Ltd Shanghai Peoples R China Univ Penn Dept Chem & Biomol Engn Philadelphia PA 19104 USA Near Miss Management LLC Philadelphia PA USA

Community detection decomposes large-scale, complex networks "optimally" into sets of smaller sub-networks. It finds sub-networks that have the least inter-connections and the most intra-connections. This article presents an efficient community detection algorithm that detects community structures in a weighted network by solving a multi-objective optimization problem. The whale optimization algorithm is extended to enable it to handle multi-objective optimization problems with discrete variables and to solve the problems on parallel processors. To this end, the population's positions are discretized using a transfer function that maps real variables to discrete variables, the initialization steps for the algorithm are modified to prevent generating unrealistic connections between variables, and the updating step of the algorithm is redefined to produce integer numbers. To identify the community configurations that are Pareto optimal, the non-dominated sorting concept is adopted. The proposed algorithm is tested on the Tennessee Eastman process and several benchmark community-detection problems.

关键词： community detection complex network multi‐ objective optimization whale optimization algorithm

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A novel speech emotion recognition model using mean update of particle swarm and whale optimization-based deep belief network

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DATA TECHNOLOGIES AND APPLICATIONS 2020年第3期54卷 297-322页

作者： Rajasekhar, B. Kamaraju, M. Sumalatha, V Gudlavalleru Engn Coll Gudlavalleru Krishna India Jawaharlal Nehru Technol Univ Anantapur India

Purpose Nowadays, the speech emotion recognition (SER) model has enhanced as the main research topic in various fields including human-computer interaction as well as speech processing. Generally, it focuses on utilizing the models of machine learning for predicting the exact emotional status from speech. The advanced SER applications go successful in affective computing and human-computer interaction, which is making as the main component of computer system's next generation. This is because the natural human machine interface could grant the automatic service provisions, which need a better appreciation of user's emotional states. Design/methodology/approach This paper implements a new SER model that incorporates both gender and emotion recognition. Certain features are extracted and subjected for classification of emotions. For this, this paper uses deep belief network DBN model. Findings Through the performance analysis, it is observed that the developed method attains high accuracy rate (for best case) when compared to other methods, and it is 1.02% superior to whale optimization algorithm (WOA), 0.32% better from firefly (FF), 23.45% superior to particle swarm optimization (PSO) and 23.41% superior to genetic algorithm (GA). In case of worst scenario, the mean update of particle swarm and whale optimization (MUPW) in terms of accuracy is 15.63, 15.98, 16.06% and 16.03% superior to WOA, FF, PSO and GA, respectively. Under the mean case, the performance of MUPW is high, and it is 16.67, 10.38, 22.30 and 22.47% better from existing methods like WOA, FF, PSO, as well as GA, respectively. Originality/value This paper presents a new model for SER that aids both gender and emotion recognition. For the classification purpose, DBN is used and the weight of DBN is used and this is the first work uses MUPW algorithm for finding the optimal weight of DBN model.

关键词： Emotion recognition Deep belief network whale optimization algorithm Styling Gender recognition

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Detection of skin cancer with adaptive fuzzy classifier using improved whale optimization

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BIOMEDICAL ENGINEERING-BIOMEDIZINISCHE TECHNIK 2020年第5期65卷 605-619页

作者： Durgarao, Nagayalanka Sudhavani, Ghanta Acharya Nagarjuna Univ Dept ECE Guntur Andhra Pradesh India RVR & JC Coll Engn Dept ECE Guntur Andhra Pradesh India

Skin cancer is considered as a well-known type of cancer globally, and its occurrence has been found to be raised in current days. Researchers state that the disease requires early prediction so that the identification of precise signs will make it simple for the dermatologists and clinicians. This disorder has been established to be unpredictable. Hence, this paper intends to develop an efficient skin cancer detection scheme, which classifies the nature of cancer, whether it is normal, benign or malignant. Accordingly, the skin image which is given as input is segmented using k-means clustering model and the features are extracted from segmented image using Local Vector Pattern (LVP). Moreover, the extracted features are subjected to fuzzy classifier for recognizing the cancer. In addition, the limits of membership functions are optimally selected by improved whale optimization algorithm (WOA). Thus, the proposed scheme is termed as Improved Selection of Encircling and Spiral updating position of WO-based Fuzzy Classifier (ISESW-FC). From the optimized output, the type of skin cancer image can be determined, whether it is normal, benign or malignant. The performance of proposed model is compared over other conventional methods, and its efficiency is proved by means of Type I and Type II measures.

关键词： k-means clustering local vector pattern skin cancer whale optimization algorithm

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Rolling bearing fault diagnosis based on RQA with STD and WOA-SVM

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HELIYON 2024年第4期10卷 e26141页

作者： Qiu, Wentao Wang, Bing Hu, Xiong Shanghai Maritime Univ Shanghai 201306 Peoples R China

A rolling bearing fault diagnosis method based on Recursive Quantitative Analysis (RQA) combined with time domain feature extraction and whale optimization algorithm Support Vector Machine (WOA-SVM) is proposed. Firstly, the recurrence graph of the vibration signal is drawn, and the nonlinear feature parameters in the recurrence graph combined with Standard Deviation (STD) are extracted by recursive quantitative analysis method to generate feature vectors;after that, in order to construct the optimal support vector machine model, the whale optimization algorithm is used to optimize the c and g parameters. Finally, both Recursive Quantitative Analysis and standard deviation are combined with the WOA-SVM model to perform fault diagnosis of rolling bearings. The rolling bearing datasets from Case Western Reserve University and Jiangnan University were used for example analysis, and the fault identification accuracy reached 100% and 95.00%, respectively. Compared to other methods, the method proposed in this paper has higher diagnostic accuracy and wide practical applicability, and the risk of accidents can be reduced through accurate fault diagnosis, which is also important for safety and environmental policies. This research originated in the field of mechanical fault diagnosis to solve the problem of fault diagnosis of rolling bearings in industrial production, it builds on previous research and explores new methods and techniques to fill some gaps in the field of mechanical fault diagnosis.

关键词： Recursive quantitative analysis Standard deviation whale optimization algorithm Support vector machine Rolling bearing Fault diagnosis

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Analytical study on optimized configuration strategy of electrochemical energy storage system under multiple scenarios

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Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Li, Tou Zhang, Wei Zhang, Xuan School of Electrical Engineering Nanjing Institute of Technology Jiangsu Nanjing210000 China Nanjing Weilixuan Information Technology Co. Ltd Jiangsu Nanjing210000 China

This paper models the electrochemical energy storage system and proposes a control method for three aspects, such as battery life, to generate a multiobjective function for optimizing the capacity allocation of electrochemical energy storage under multiple scenarios, with conditional constraints on the system, storage, and progression aspects. The improved whale optimization algorithm is used to solve the multi-objective function to find the most reasonable electrochemical energy storage system capacity optimization allocation scheme. Using the model constructed in this paper under multi-scenario conditions, it is found after solving that the optimal allocation scheme purchases power from the grid at around 25MW during the highest peak hours in summer and 5MW in winter, which ensures the economic benefits. Meanwhile, the maximum power fluctuation of the electrochemical energy storage system at point A of the optimization strategy provided by the model is only 2.16%, which is much lower than the preset 4.32%, so the optimal allocation strategy reaches the optimum. Comparing the performance of configured energy storage in different scenarios, the peak-valley power difference of the model proposed in this paper decreases from 11.6 MW to 8.9 MW, which is a better performance than that of the control group, which is 10.8 MW-9.1 MW, and the effect of peak shaving and valley filling is obvious. © 2024 Tou Li, Wei Zhang and Xuan Zhang, published by Sciendo.

关键词： Electrochemical energy storage Multi-objective function Optimal allocation whale optimization algorithm

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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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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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A hybrid framework for predicting the remaining useful life of battery using Gaussian process regression

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JOURNAL OF ENERGY STORAGE 2023年 66卷

作者： Li, Jiabo Ye, Min Wang, Yan Wang, Qiao Wei, Meng Xian Shiyou Univ Mech Engn Coll Xian 710065 Peoples R China Changan Univ Engn Res Ctr Expressway Construct & Maintenance Eq Xian 710064 Peoples R China

The battery will inevitably deteriorate in its service life. Therefore, accurate prediction of the remaining useful life (RUL) is critical to the durability of the battery energy storage systems (ESS). The RUL prediction framework of Gaussian process regression(GPR) based on automatic stack autoencoder (SAE) and improved whale opti-mization algorithm (WOA) is proposed in this research paper. Firstly, the SAE and gray relation analysis are proposed to extract indirect health indicators (HIs) from battery degradation data. Secondly, the Gaussian process regression (GPR) approach combined with the WOA method to establish the RUL prediction framework. Besides, aiming at the disadvantage that WOA is prone to fall into local optimization, the WOA based on the sine cosine algorithm of decentralized foraging strategy is proposed to optimize the prediction ability of the GPR. Finally, compared with the prediction results of existing approaches under the same conditions, the simulation results indicate that the RUL prediction errors based on the proposed algorithm are controlled within 2 %, which has good advantages and prediction ability.

关键词： Remaining useful life Energy storage systems Gaussian process regression whale optimization algorithm Health indicators

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MTMC-AUR2CNet: Multi-textural multi-class attention recurrent residual convolutional neural network for COVID-19 classification using chest X-ray images

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第1期85卷 104857页

作者： Gopatoti, Anandbabu Vijayalakshmi, P. Hindusthan Coll Engn & Technol Dept Elect & Commun Engn Coimbatore Tamil Nadu India Anna Univ Ctr Res Chennai Tamil Nadu India

Coronavirus disease (COVID-19) has infected over 603 million confirmed cases as of September 2022, and its rapid spread has raised concerns worldwide. More than 6.4 million fatalities in confirmed patients have been reported. According to reports, the COVID-19 virus causes lung damage and rapidly mutates before the patient receives any diagnosis-specific medicine. Daily increasing COVID-19 cases and the limited number of diagnosis tool kits encourage the use of deep learning (DL) models to assist health care practitioners using chest X-ray (CXR) images. The CXR is a low radiation radiography tool available in hospitals to diagnose COVID-19 and combat this spread. We propose a Multi-Textural Multi-Class (MTMC) UNet-based Recurrent Residual Convolutional Neural Network (MTMC-UR2CNet) and MTMC-UR2CNet with attention mechanism (MTMC-AUR2CNet) for multi-class lung lobe segmentation of CXR images. The lung lobe segmentation output of MTMC-UR2CNet and MTMC-AUR2CNet are mapped individually with their input CXRs to generate the region of interest (ROI). The multi-textural features are extracted from the ROI of each proposed MTMC network. The extracted multi-textural features from ROI are fused and are trained to the whale optimization algorithm (WOA) based DeepCNN classifier on classifying the CXR images into normal (healthy), COVID-19, viral pneumonia, and lung opacity. The experimental result shows that the MTMC-AUR2CNet has superior performance in multi-class lung lobe segmentation of CXR images with an accuracy of 99.47%, followed by MTMC-UR2CNet with an accuracy of 98.39%. Also, MTMC-AUR2CNet improves the multi-textural multi-class classification accuracy of the WOA-based DeepCNN classifier to 97.60% compared to MTMC-UR2CNet.

关键词： Chest X-ray Multi-class lung lobe segmentation Multi-textural features Attention mechanism whale optimization algorithm Multi-class classification

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Near infrared spectroscopic variable selection by a novel swarm intelligence algorithm for rapid quantification of high order edible blend oil

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023年 284卷 121788页

作者： Bian, Xihui Zhang, Rongling Liu, Peng Xiang, Yang Wang, Shuyu Tan, Xiaoyao Tiangong Univ Sch Chem Engn & Technol State Key Lab Separat Membranes & Membrane Proc Tianjin 300387 Peoples R China Yibin Univ Sichuan Univ Key Lab Proc Anal & Control Yibin 644000 Sichuan Peoples R China Qinghai Univ State Key Lab Plateau Ecol & Agr Xining 810016 Peoples R China

The quantification of single oil in high order edible blend oil is a challenging task. In this research, a novel swarm intelligence algorithm, discretized whale optimization algorithm (WOA), was first developed for reducing irrelevant variables and improving prediction accuracy of hexanary edible blend oil samples. The WOA is inspired by hunting strategy of humpback whales, which mainly includes three behaviors, i.e., encircling prey, bubble-net attacking and searching for prey. In discretized WOA, positions of whales were updated and then discretized by arctangent function. The whale population performance, iteration number and whale number of WOA were investigated. To validate the performance of selected variables, partial least squares (PLS) was used to build model and predict single oil contents in hexanary blend oil. Results show that WOA-PLS can provide the best prediction accuracy compared with full-spectrum PLS, continuous wavelet transform-PLS (CWT-PLS), un-informative variable elimination-PLS (UVE-PLS), Monte Carlo uninformative variable elimination-PLS (MCUVE-PLS) and randomization test-PLS (RT-PLS). Furthermore, CWT-WOA-PLS can further produce better results with fewer variables compared with WOA-PLS.

关键词： Edible blend oil Spectral analysis Variable selection Multivariate calibration whale optimization algorithm

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