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Feature subset selection for predicting the success of crowdfunding project campaigns

ELECTRONIC MARKETS 2021年第3期31卷 671-684页

作者： Ryoba, Michael J. Qu, Shaojian Zhou, Yongyi Univ Shanghai Sci & Technol Sch Business Shanghai Peoples R China

Statistics from crowdfunding platforms show that a small percent of crowdfunding projects succeed in securing funds. This makes project creators eager to know the probability of success of their campaign and the features that contribute to its success before launching it on crowdfunding platforms. The existing literature focuses on examining success probability using the entire list of identified projects features. For situations for which project creators have limited resources to invest on the required project features, the list suggested by previous researchers is somewhat large and gives a small success probability. A minimal number of features that predict success with a higher probability can benefit project creators by providing them with insight and guidance in investing their limited resources. This paper presents a metaheuristic whale optimization algorithm (WOA) in the crowdfunding context to perform a complete search of a subset of features that have a high success contribution power. Experiments were conducted using WOA with the K-Nearest Neighbor (KNN) classifier on a Kickstarter dataset. Our approach obtains a subset of 9 features that predict the success of project campaigns with an accuracy (F-score) of 90.28% (90.11%), which is an increase (F-score) of 22.23% (21.61%) than when a complete set of features is used. The findings of this study contribute knowledge to various crowdfunding stakeholders, as they will provide new insights regarding a subset of essential features th4at influence the success of project campaigns with high accuracy.

关键词： Crowdfunding Crowdfunding prediction Machine learning Feature selection whale optimization algorithm

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Non-linear static and dynamic buckling analysis of laminated composite cylindrical shell embedded in non-linear elastic foundation using the swarm-based metaheuristic algorithms

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EUROPEAN JOURNAL OF MECHANICS A-SOLIDS 2022年 91卷 1页

作者： Foroutan, Kamran Varedi-Koulaei, Seyyed Mojtaba Duc, Nguyen Dinh Ahmadi, Habib Shahrood Univ Technol Fac Mech & Mechatron Engn Shahrood Iran Hanoi Univ Engn & Technol UET Vietnam Natl Univ Dept Technol & Engn Construct & Transportat Hanoi Vietnam Nguyen Tat Thanh Univ NTT Inst High Technol Dist 4 Ho Chi Minh City Vietnam Sejong Univ Dept Civil & Environm Engn Natl Res Lab 209 Neungdong Ro Seoul 05006 South Korea

In this paper, by determining the optimal fiber's angles for maximum critical buckling load, the non-linear static and dynamic buckling analyses of laminated composite cylindrical shells resting on non-linear elastic foundation subjected to external loading are investigated. The laminated composite cylindrical with three, four, and five layers is considered. In the present study, first, the optimal fiber's angles are found using the swarm-based metaheuristic algorithms, then, the non-linear static and dynamic buckling analyses are carried out at optimum fiber directions. According to classical shell theory, von-Karman equation, and Hooke's law, the stressstrain relations are derived for the laminated composite cylindrical shell. Then, based on the Galerkin's method, the discretized motion equation is obtained. For the optimization problem, the design variables are ply angles, and the objective function is maximizing the critical buckling load. Four recently developed swarm-based metaheuristic algorithms including, grey wolf optimizer (GWO), whale optimization algorithm (WOA), grasshopper optimization algorithm (GOA), and salp swarm algorithm (SSA), are utilized to solve these optimization problems. Based on the results, the optimization algorithms have improved the value of the critical buckling load significantly. Also, the WOA algorithm had better results than the others, while the GOA was the weakest.

关键词： Laminated composite cylindrical shell Non-linear elastic foundation Grey wolf optimizer whale optimization algorithm Grasshopper optimization algorithm Salp swarm algorithm Non-linear static and dynamic buckling

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Evolutionary inspired approach for mental stress detection using EEG signal

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期197卷 116634-116634页

作者： Sharma, Lakhan Dev Bohat, Vijay Kumar Habib, Maria Al-Zoubi, Ala' M. Faris, Hossam Aljarah, Ibrahim VIT AP Univ Sch Elect Engn Amaravati India Netaji Subhas Univ Technol Dept Comp Sci & Engn Delhi India Altibbi Amman Jordan Univ Jordan Informat Technol Dept Amman Jordan Univ Granada Sch Sci Technol & Engn Granada Spain Al Hussein Tech Univ Sch Comp & Informat Amman Jordan Univ Granada Univ Granada CITIC UGR Res Ctr Informat & Commun Technol Granada Spain

Stress is a pensive issue in our competitive world and it has a huge impact on physical and mental health. Severe health issues may arise due to long exposure of stress. Hence, its timed detection can be helpful in managing stress periods. In this regard, electroencephalogram (EEG) based techniques have been widely explored, as stress severely impact the functioning and structure of brain. These non-invasive methods for stress detection need improvement in terms of predictive accuracy and reliability. In this work, a novel approach for stress detection has been presented using short duration of EEG signal. Entropy based features were extracted from EEG signal decomposed using stationary wavelet transform. Selected features were used for classification using different supervised machine learning algorithms. Further, different evolutionary inspired approaches were deployed to optimize the parameters of support vector machines (SVM) and perform feature weighting, simultaneously. SVM optimized using whale optimization algorithm resulted in an accuracy of 97.2559%. Accurate detection using short duration EEG signal shows potential of this technique for timed and reliable detection of stress.

关键词： Mental stress Electroencephalogram Stationary wavelet transform Evolutionary inspired whale optimization algorithm Support vector machine

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Information sharing search boosted whale optimizer with Nelder-Mead simplex for parameter estimation of photovoltaic models

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ENERGY CONVERSION AND MANAGEMENT 2022年 270卷

作者： Peng, Lemin He, Caitou Heidari, Ali Asghar Zhang, Qian Chen, Huiling Liang, Guoxi Aljehane, Nojood O. Mansour, Romany F. Wenzhou Univ Dept Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China Wenzhou Univ Technol Sch Data Sci & Artificial Intelligence Wenzhou 325035 Zhejiang Peoples R China Dept Informat Technol Wenzhou Polytech Wenzhou 325035 Peoples R China Univ Tabuk Fac Comp & Informat Technol Tabuk Saudi Arabia New Valley Univ Fac Sci Dept Math El Kharga 72511 Egypt

With the recent emphasis on new energy sources, solar photovoltaic cells have received widespread attention from scholars as a highly efficient and clean new energy source. Researchers model solar photovoltaic cells and analyze the values of various unknown parameters to improve their efficiency in converting solar energy into electric energy. To extract unknown parameters more accurately and efficiently, we propose a whale optimi-zation algorithm (WOA) driven by the information sharing search mechanism and the Nelder-Mead simplex (NMs) mechanism, which ISNMWOA. The WOA can perform a global search of the parameters of the photo-voltaic model to obtain a number of initial vectors of model parameters that satisfy the parameter range. The information sharing search strategy performs a further coarse local search on a number of feasible solutions generated. In contrast, the NMs can perform a further refined local search in the vicinity of the optimal pa-rameters to obtain the final vector of optimally performing model parameters. First, we tested ISNMWOA against six advanced algorithms in CEC 2014. We then used the ISNMWOA to evaluate the parameters of the solar cells and PV modules. The RMSE of ISNMWOA on single diode model (SDM), double diode model (DDM), three diode model (TDM), and photovoltaic module model (PV) are 9.8602E-04, 9.8248E-04, 9.8248E-04 and 2.4251E-03 respectively. Compared to the original WOA, the RMSE of ISNMWOA on SDM, DDM, TDM, and PV decreased by 30.01%, 24.24%, 50.98%, and 6.94%, respectively. Moreover, compared to the high-performing GOFPANM, ISNMWOA runs 298 times faster than GOFPANM with almost identical optimization results. We evaluated the performance of three commercial PV modules under different irradiance and temperature conditions using ISNMWOA to verify the reliability of the experiment. The experimental results show that the ISNMWOA out-performs similar algorithms currently available.

关键词： whale optimization algorithm optimization Swarm -intelligence Parameter estimation Photovoltaic models Solar energy

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Study on the Modeling and Compensation Method of Pose Error Analysis for the Fracture Reduction Robot

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MICROMACHINES 2022年第8期13卷 1186-1186页

作者： Liu, Minghe Li, Jian Sun, Hao Guo, Xin Xuan, Bokai Ma, Lifang Xu, Yuexuan Ma, Tianyi Ding, Qingsong An, Baichuan Hebei Univ Technol Sch Artificial Intelligence & Data Sci & Engn Res Minist Educ Tianjin 300130 Peoples R China Beijing Univ Posts & Telecommun Sch Automat Beijing 100876 Peoples R China Natl Res Ctr Rehabil Tech Aids Beijing Key Lab Rehabil Tech Aids Old Age Disabil Beijing 100176 Peoples R China Natl Res Ctr Rehabil Tech Aids Key Lab Neurofunct Informat & Rehabil Engn Minist Civil Affairs Beijing 100176 Peoples R China

Background: In the process of fracture reduction, there are some errors between the actual trajectory and the ideal trajectory due to mechanism errors, which would affect the smooth operation of fracture reduction. To this end, based on self-developed parallel mechanism fracture reduction robot (FRR), a novel method to reduce the pose errors of FRR is proposed. Methods: Firstly, this paper analyzed the pose errors, and built the model of the robot pose errors. Secondly, mechanism errors of FRR were converted into drive bar parameter's errors, and the influence of each drive bar parameter on the robot pose error were analyzed. Thirdly, combining with Cauchy opposition-based learning and differential evolution algorithm (DE), an improved whale optimization algorithm (CRLWOA-DE) is proposed to compensate the end-effector's pose errors, which could improve the speed and accuracy of fracture reduction, respectively. Results: The iterative accuracy of CRLWOA-DE is improved by 50.74%, and the optimization speed is improved by 22.62% compared with the whale optimization algorithm (WOA). Meanwhile, compared with particle swarm optimization (PSO) and ant colony optimization (ACO), CRLWOA-DE is proved to be more accurate. Furthermore, SimMechanics in the software of MATLAB was used to reconstruct the fracture reduction robot, and it was verified that the actual motion trajectory of the CRLWOA-DE optimized kinematic stage showed a significant reduction in error in both the x-axis and z-axis directions compared to the desired motion trajectory. Conclusions: This study revealed that the error compensation in FRR reset process had been realized, and the CRLWOA-DE method could be used for reducing the pose error of the fracture reduction robot, which has some significance for the bone fracture and deformity correction.

关键词： fracture reduction robot error model whale optimization algorithm differential evolution algorithm opposition-based learning error compensation

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Optimal operation of multi-source electric vehicle connected microgrid using metaheuristic algorithm

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JOURNAL OF ENERGY STORAGE 2022年 52卷

作者： Jayalakshmi, N. S. Jadoun, Vinay Kumar Gaonkar, D. N. Shrivastava, Ashish Kanwar, Neeraj Nandini, K. K. Manipal Acad Higher Educ Manipal Inst Technol Dept Elect & Elect Engn Manipal 576104 Karnataka India NITK Dept Elect & Elect Engn Mangalore 575025 India Shri Vishwakarma Skill Univ Skill Fac Engn & Technol Gurugram 122003 Haryana India Manipal Univ Jaipur Dept Elect Engn Jaipur 303007 Rajasthan India

In this paper, a multi-source microgrid (MG) has been considered which inducts power from solar photovoltaic (PV), wind turbine, pumped hydro storage system (PHSS) and diesel generator (DG). A problem formulation is proposed on a multi-source MG considering an electric vehicle (EV) as source and load demand. A modified operation strategy is proposed to achieve the lowest possible fuel usage of DG and to optimize the operation of multi-sources used in the MG. When the sum of PV, wind power production and EV discharge is less than the load requirement, the required deficit power should be delivered by DG and PHS. This work considers PV and wind as the primary energy supplying sources, while DG, EV and PHS as the additional energy suppliers with EV and PHS as energy storage systems. By properly coordinating EVs, they can become a major contributor to the successful execution of the MG concept. In this work, a modified charging/discharging algorithm is presented to check the effect of EVs to supply a portion of peak loads with PHS to reduce the fuel consumption of DG in three diverse modes of operation. A modified whale optimization algorithm (WOA) and teaching learning-based optimization (TLBO) are applied to effectively solve this proposed complex problem using the MATLAB platform. The optimum solutions obtained after different independent trials by both the techniques are compared with the latest published techniques. It can be observed that modified WOA performs better than TLBO and other recently published methods on the base case and proposed multi-source MG case in three diverse modes of operation. The outcomes of the simulation confirm the effectiveness of modified WOA in reducing fuel consumption.

关键词： Multi-source microgrid Electric vehicle whale optimization algorithm Teaching-learning based optimization Pumped hydro storage Wind power Solar power Diesel generators

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Evaluation of bio-inspired optimization algorithms hybrid with artificial neural network for reference crop evapotranspiration estimation

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2021年 190卷 106466-106466页

作者： Gao, Lili Gong, Daozhi Cui, Ningbo Lv, Min Feng, Yu Chinese Acad Agr Sci Inst Environm & Sustainable Dev Agr State Engn Lab Efficient Water Use Crops & Disast Beijing Peoples R China Sichuan Univ State Key Lab Hydraul & Mt River Engn Chengdu Peoples R China Sichuan Univ Coll Water Resource & Hydropower Chengdu Peoples R China Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid & Semiarid Are Yangling Shaanxi Peoples R China Southern Univ Sci & Technol Sch Environm Sci & Engn Shenzhen Peoples R China

Reference crop evapotranspiration (ETo) is a determinant factor in agricultural water resource management. Therefore, accurate ETo information is critical to quantify crop water requirements for precision agriculture management. This study coupled bio-inspired optimization algorithms with artificial neural network (ANN), i.e., ANN with bat algorithm (BA-ANN), ANN with cuckoo search algorithm (CSA-ANN), and ANN with whale optimization algorithm (WOA-ANN), and developed three hybrid ANN models for daily ETo modeling with limited inputs. The models were trained and evaluated using a k-fold test approach and long-term daily climatic data from 2001 to 2018 at six climatic stations in the Loess Plateau of north China. Three input scenarios were used, including temperature-based inputs, radiation-based inputs, and mass transfer-based inputs. The statistical comparison showed that the hybrid WOA-ANN offered better estimates than BA-ANN and CSA-ANN in all three input scenarios. In general, the radiation-based WOA-ANN provided the most accurate ETo estimations, with regional average relative root mean square error and Nash-Sutcliffe efficiency coefficient of 13.3% and 0.959, respectively. The temperature-based WOA-ANN offered acceptable and reasonable ETo estimates. Thus, it is a reliable tool for ETo modeling, given that air temperature is available in many regions. Overall, the bio-inspired optimization algorithms are robust tools for enhancing ANN performance in ETo simulation, and thus they are highly recommended to estimate ETo in the study region. Our study proposed powerful models for accurately estimating ETo with limited inputs, offering practical implications for the development of precision agriculture.

关键词： Machine learning Evapotranspiration Agricultural water resources Precision agriculture whale optimization algorithm

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Research on IMDB Film Score Prediction Based on Improved whale algorithm

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Procedia Computer Science 2022年 208卷 361-366页

作者： Cong Zhang Film College of Changchun Guanghua University China

In order to improve the prediction precision of film score, the improved whale algorithm is established to predict the IMDB film score. Firstly, the IMDB scoring mechanism is studied, the reason of inaccurate scoring or network "water army" is analyzed, and the score of the meta score, the number of reviews and the popularity index are discussed. Data of the film in system is updated in real time. Secondly, the improved whale optimization algorithm is constructed, the mathematical model of encircling prey, bubble net attacking and search for prey are discussed. The adaptive weight formula is established and the improved adaptive updating formula is constructed, and the corresponding algorithm procedure is designed. Finally, through the analysis of visual results, the key factors affecting film scoring are found out, and suggestions are given for the film recommendation system. One hundred films are selected to carry out prediction analysis, the top 85% films are used as training samples, and the bottom 15% films are used as testing samples. The prediction mode is trained through training samples, and the testing samples are predicted based on trained prediction model. A prediction simulation analysis is carried out using IMDB samples, and results show that the proposed prediction model has high prediction precision.

关键词： IMDB film score whale optimization algorithm prediction model scoring mechanism precision

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The Identification of ECG Signals Using Wavelet Transform and WOA-PNN

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SENSORS 2022年第12期22卷 4343-4343页

作者： Li, Ning He, Fuxing Ma, Wentao Wang, Ruotong Jiang, Lin Zhang, Xiaoping Xian Univ Technol Sch Elect Engn Xian 710048 Peoples R China Minist Educ Key Lab Control Power Transmiss & Convers SJTU Shanghai 200240 Peoples R China Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ Merseyside England Univ Birmingham Dept Elect Elect & Syst Engn Sch Engn Birmingham B15 2TT W Midlands England

Electrocardiogram (ECG) signal identification technology is rapidly replacing traditional fingerprint, face, iris and other recognition technologies, avoiding the vulnerability of traditional recognition technologies. This paper proposes an ECG signal identification method based on the wavelet transform algorithm and the probabilistic neural network by whale optimization algorithm (WOA-PNN). Firstly, Q, R and S waves are detected by wavelet transform, and the P and T waves are detected by local windowed wavelet transform. The characteristic values are constructed by the detected time points, and the ECG data dimension is smaller than that of the non-reference detection. Secondly, combined with the probabilistic neural network, the mean impact value algorithm is used to screen the characteristic values, the characteristic values with low influence are eliminated, and the input and complexity of the model are simplified. Finally, a WOA-PNN combined classification method is proposed to intelligently optimize the hyper parameters in the probabilistic neural network algorithm to improve the model accuracy. According to the simulation verification on three databases, ECG-ID, MIT-BIH Normal Sinus Rhythm and MIT-BIH Arrhythmia, the identification accuracy of a single ECG cycle is 96.97%, and the identification accuracy of three ECG cycles is 99.43%.

关键词： electrocardiogram signal identification wavelet transform probabilistic neural network mean impact value whale optimization algorithm

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Electricity Demand Forecasting With a Modified Extreme-Learning Machine algorithm

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Chen, Chen Ou, Chuangang Liu, Mingxiang Zhao, Jingtao NARI Technol Co Ltd Nanjing Peoples R China

To operate the power grid safely and reduce the cost of power production, power-load forecasting has become an urgent issue to be addressed. Although many power load forecasting models have been proposed, most still suffer from poor model training, limitations sensitive to outliers, and overfitting of load forecasts. The limitations of current load-forecasting methods may lead to the generation of additional operating costs for the power system, and even damage the distribution and network security of the related systems. To address this issue, a new load prediction model with mixed loss functions was proposed. The model is based on Pinball-Huber's extreme-learning machine and whale optimization algorithm. In specific, the Pinball-Huber loss, which is insensitive to outliers and largely prevents overfitting, was proposed as the objective function for extreme-learning machine (ELM) training. Based on the Pinball-Huber ELM, the whale optimization algorithm was added to improve it. At last, the effect of the proposed hybrid loss function prediction model was verified using two real power-load datasets (Nanjing and Taixing). Experimental results confirmed that the proposed hybrid loss function load prediction model can achieve satisfactory improvements on both datasets.

关键词： outliers whale optimization algorithm load forecasting Pinball-Huber regression extreme-learning machine

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