Comparative study on inversion of the unsaturated hydraulic parameters using optimization and Bayesian estimation methods by KE Fengqiao, MAN Jun, ZENG Lingzao, WU Laosheng

“…However, this method is sensitive to the initial guess of parameters, and the obtained predictions occasionally deviate from the measurements. 2) The MCMC algorithm can provide state predictions which better fit measurements. …”
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An Improved Kernel Based Extreme Learning Machine for Robot Execution Failures by Bin Li, Xuewen Rong, Yibin Li

“…For improving the prediction accuracy of robot execution failures, this paper proposes a novel KELM learning algorithm using the particle swarm optimization approach to optimize the parameters of kernel functions of neural networks, which is called the AKELM learning algorithm. …”
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Evaluation Model of Low-Carbon Circular Economy Coupling Development in Forest Area Based on Radial Basis Neural Network by Chang Liu

“…In this paper, we study the radial neural network algorithm for low-carbon circular economy in forest area, design a coupled development evaluation model, study its algorithmic ideas operation mode and the update formula obtained by standard algorithm, and finally optimize the RBF neural network by particle swarm algorithm. …”
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Forecasting Financial Crashes: Revisit to Log-Periodic Power Law by Bingcun Dai, Fan Zhang, Domenico Tarzia, Kwangwon Ahn

“…We aim to provide an algorithm to predict the distribution of the critical times of financial bubbles employing a log-periodic power law. …”
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Tasmanian devil whale optimization (TDWO) is introduced for secure video transmission in 5G networks. by Fengren Lin, Minrong Lu

“…Here, the recorded educational videos are considered and are transmitted over 5G network transmission resources initially. …”
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A Novel Method of Self-Healing Concrete to Improve Durability and Extend the Service Life of Civil Infrastructure by Yan Xue, Weiliang Gao, Yanming Zhao

“…Building upon this, the enhanced concrete durability prediction model based on the NSGA-II algorithm proves to be highly effective in predicting the optimal concrete mix proportion scheme. …”
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Soft Measurement of Wastewater Treatment System Based on PSOGA-WNN by LIU Yuhui, MAI Wenjie, LI Xiaoyong, ZHAO Yinzhong, HE Xinzhong, HUANG Mingzhi

“…To accurately predict the SS_eff (effluent SS) content and COD_eff (effluent COD) concentration in water quality parameters and further improve the water quality early warning mechanism,this paper proposes the PSOGA-WNN soft measurement model of paper wastewater effluent quality to obtain the main water quality technical parameters,COD_inf (influent COD),Q (influent flow),pH (influent pH),SS_inf (influent SS),T (influent temperature),DO (influent dissolved oxygen),COD_eff,and SS_eff, for predicting the quality of wastewater from the wastewater treatment plant.Among them,the prediction results of PSOGA-WNN are compared with the neural networks of PSO-WNN,GA-WNN,and PSOGA-BP.The results show that the PSOGA-WNN neural network has the highest prediction accuracy,which indicates that the PSOGA hybrid parameter optimization algorithm based on the genetic algorithm and particle swarm algorithm has obvious superiority in optimizing the prediction accuracy of the model.The WNN neural network has certain advantages over BP neural network in terms of fitting degree as well as error accuracy and is an effective means of simulation prediction.…”
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Joint Allocation of Power and Subcarrier for Low Delay and Stable Power Line Communication by Zhixiong Chen, Zhihui Yang, Zeng Dou

“…To meet the requirements of low-latency services such as remote control and demand-side response, a joint optimal allocation algorithm of subcarriers and their power based on diversity grouping and channel prediction is proposed. …”
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An Adaptive Evolutionary Causal Dynamic Factor Model by Qian Wei, Heng-Guo Zhang

“…Results: The experimental results show that the AcNowcasting algorithm can extract common factors that reflect macroeconomic fluctuations better, and the prediction accuracy of the AcNowcasting algorithm is more accurate than that of traditional nowcasting models. …”
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Project quality, regulation quality by Elena Mussinelli

“…Instead, deductive design approaches seem to prevail today, due to the growing availability of algorithmic procedures that do not merely support the design process, but develop it in an almost automated manner through conditioning and prevailing indicators and parameters. …”
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Rolling Bearing Remaining Useful Life Prognosis Method based on Improved CHSMM by Zhu Shuo, Bai Ruilin, Ji Feng

“…The experimental results show that the proposed method can accurately predict the remaining useful life of bearings.Compared with the original CHSMM algorithm,the accuracy of the degradation state recognition is increased by12%,and the accuracy of remaining useful life prediction is increased by 23%.…”
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Parameter Optimization Method for Predictor–Corrector Guidance With Impact Angle Constraint by Xinwan Kong, Cheng Zhang

“…Additionally, an uncertainty factor is proposed to describe the model uncertainty in the predictor–corrector guidance algorithm. Based on the uncertainty factor, the impact of uncertainty and external disturbances on prediction accuracy is derived, and the propagation of prediction error to the miss distance is analyzed using the adjoint method. …”
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L-Shaped-Sensor-Array-Based Localization and Tracking Method for 3D Maneuvering Target by Xing Zhang, Xue Wang

“…Thirdly an autoregressive (AR) particle filter (PF) algorithm is realized to predict the locations in the next moment. …”
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Student Engagement Recognition: Comprehensive Analysis Through EEG and Verification by Image Traits Using Deep Learning Techniques by Ajitha Sukumaran, Arun Manoharan

“…In this paper, we propose an engagement recognition system that detects student engagement using EEG signals by integrating levels of valence and arousal with the Russel 2D circumplex model using deep learning algorithm. The public DEAP dataset was used for training the model to predict valence and arousal values. …”
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Energy Services Demand Forecasting Combined with Feature Preferences and Bidirectional Long- and Short-Term Memory Networks by KANG Feng, TAN Huochao, SU Liwei, JIAN Donglin, WANG Shuai, QIN Hao, ZHANG Yongjun

“…Therefore, this paper proposes a user energy service demand prediction model based on feature selection. The methodology includes introducing a sampling algorithm to solve the class imbalance problem in the data on the basis of analysing the user energy service data, reducing the dimensionality of the data based on an autoencoder to ensure efficient clustering of the K-mean algorithm, constructing a feature selection algorithm based on a lightweight gradient lifting machine to filter the effective features and improve the training efficiency of the prediction model, and establishing a bidirectional long- and short-term memory neural network multi-label predicting model based on an attentional mechanism to refine the user’s energy service demand. …”
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Short-Term Load Forecasting Based on EEMD-WOA-LSTM Combination Model by Lei Shao, Quanjie Guo, Chao Li, Ji Li, Huilong Yan

“…Compared with the prediction results of EEMD-ARMA model, RNN model, LSTM model, and WOA-LSTM model, the combined prediction model optimized by whale bionics has less prediction error and higher prediction accuracy.…”
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Detection of Low-Flying Target under the Sea Clutter Background Based on Volterra Filter by Hongyan Xing, Yan Yan

“…In the cases of low SNR, after de-noised by joint algorithm, Volterra prediction model can also detect the low-flying small target clearly.…”
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A Novel Model with GA Evolving FWNN for Effluent Quality and Biogas Production Forecast in a Full-Scale Anaerobic Wastewater Treatment Process by Zehua Huang, Renren Wu, XiaoHui Yi, Hongbin Liu, Jiannan Cai, Guoqiang Niu, Mingzhi Huang, Guangguo Ying

“…The analysis results indicate that the FWNN with the optimal algorithm had a high speed of convergence and good quality of prediction, and the FWNN model was more advantageous than the traditional intelligent coupling models (NN, WNN, and FNN) in prediction accuracy and robustness. …”
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An integrated approach of feature selection and machine learning for early detection of breast cancer by Jing Zhu, Zhenhang Zhao, Bangzheng Yin, Canpeng Wu, Chan Yin, Rong Chen, Youde Ding

“…With the increasing application of machine learning technology in the medical field, algorithm-based diagnostic tools provide new possibilities for early prediction of breast cancer. …”
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Supervised Machine Learning for Classification of the Electrophysiological Effects of Chronotropic Drugs on Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes. by Christopher Heylman, Rupsa Datta, Agua Sobrino, Steven George, Enrico Gratton

“…The results demonstrate the ability of our algorithm to accurately assess, classify, and predict hiPS-CM membrane depolarization following exposure to chronotropic drugs.…”
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