Full Information Numerical Simulation of Two-Dimensional Steady Ground Temperature Field by Fan Jiang, Shikun Dai, Ying Zhang, Hongjun Tian

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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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Intelligent optimization method of fracturing parameters for shale oil reservoirs in Jimsar Sag, Junggar Basin, NW China by Yunjin WANG, Fujian ZHOU, Hang SU, Leyi ZHENG, Minghui LI, Fuwei YU, Yuan LI, Tianbo LIANG

“…A separated fracturing effect prediction model is proposed, with the fracturing learning curve decomposed into two parts: (1) overall trend, which is predicted by the algorithm combining the convolutional neural network with the characteristics of local connection and parameter sharing and the gated recurrent unit that can solve the gradient disappearance; and (2) local fluctuation, which is predicted by integrating the adaptive boosting algorithm to dynamically adjust the random forest weight. …”
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Engineering project management technology based on visual simulation module and particle swarm optimization by Hua Tian

“…The particle swarm multi-objective optimization algorithm performed well in reducing project cost prediction errors. …”
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Parameter Calculation of Steam Pipeline Based on Hybrid Modeling by Hongwei CHEN, Zherui MA, Chunwang LV, Yuqiang LIU, Wei ZHANG, Ruikun WANG

“…Based on the mechanism model, this paper establishes a data-driven error prediction model by virtue of the vector machine algorithm to predict the mechanism error caused by the mechanism model calculation, then uses the error prediction result of the model to correct the mechanism model calculation results. …”
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Forecast of Photovoltaic Power Based on IWPA-LSSVM Considering Weather Types and Similar Days by Yilun XU, Binqiao ZHANG, Jing HUANG, Xiao XIE, Ruoxin WANG, Danqing SHEN, Lina HE, Kaifan YANG

“…In order to improve the prediction accuracy of photovoltaic power, the input of the photovoltaic power prediction model is determined according to the characteristics of photovoltaic output power under different weather types. …”
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Research on operation optimization of heavy-haul combined trains in long and steep downhill sections based on reinforcement learning by WANG Jianhua, WANG Chunyi, ZENG Zhou, WANG Cong, WANG Qingyuan, YANG Hang

“…To mitigate longitudinal impulse and address challenge posed by continuous air braking operations in long and steep downhill sections for 20 000-ton heavy haul combined trains, this paper proposes an approach for operation optimization of such trains featuring a long formation in such sections based on a data-driven algorithm. An air braking force prediction model was developed based on neural network learning focusing on the variation rules of air braking performance across different operating states, to incorporate differences in air braking characteristics across different trains and varied braking system states on same trains. …”
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Distributed OPGW abnormal vibration monitoring and forewarning based on LSTM by Tianlong Bu, Hanpeng Kou, Dapei Zhang, Zhenhua Feng, Helen Law, Bin Wang

“…In addition, the abnormal vibration forewarning method is analyzed by correlating predicted data with historical data. Experimental results in Hulunbuir demonstrate that the LSTM algorithm performs well in predictions over a 22-h period, evidenced by a root mean square error of 0.8729 and a determination coefficient (R2) of 0.9938 for the fitting curve with actual results. …”
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Short time solar power forecasting using P-ELM approach by Shuqi Shi, Boyang Liu, Long Ren, Yu Liu

“…The results indicate that the P-ELM algorithm achieves higher accuracy in short-term prediction, demonstrating its suitability for ensuring accuracy and reliability in real-time solar power forecasting.…”
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Research on Identification Technology of Explosive Vibration Based on EEMD Energy Entropy and Multiclassification SVM by Huayuan Ma, Xinghua Li, Qiang Liu, Xie Xingbo, Chong Ji, Changxiao Zhao

“…Taking eigenvector composed of CEE (components of energy entropy) as input, multiclassification SVM algorithm was used for training and prediction. Prediction accuracy was more than 80%. …”
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Classification Of Direction Using Naive Bayes Classifier Method (Case Study Of Hidayatul Islam Leces Vocational School) by Dwi Yanto, Heri Susanto, Ninanesia Rusdiana, Kiky Zulkifli

“…In this thesis, we conducted a study on determining student majors using the Naive Bayes Classifier algorithm at SMK Hidayatul Islam. The purpose of this study was to test the accuracy of the Naive Bayes Classifier algorithm in predicting student majors and to provide recommendations that can support decision making in determining student majors. …”
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Study on privacy preserving encrypted traffic detection by Xinyu ZHANG, Bingsheng ZHANG, Quanrun MENG, Kui REN

“…Existing encrypted traffic detection technologies lack privacy protection for data and models, which will violate the privacy preserving regulations and increase the security risk of privacy leakage.A privacy-preserving encrypted traffic detection system was proposed.It promoted the privacy of the encrypted traffic detection model by combining the gradient boosting decision tree (GBDT) algorithm with differential privacy.The privacy-protected encrypted traffic detection system was designed and implemented.The performance and the efficiency of proposed system using the CICIDS2017 dataset were evaluated, which contained the malicious traffic of the DDoS attack and the port scan.The results show that when the privacy budget value is set to 1, the system accuracy rates are 91.7% and 92.4% respectively.The training and the prediction of our model is efficient.The training time of proposed model is 5.16 s and 5.59 s, that is only 2-3 times of GBDT algorithm.The prediction time is close to the GBDT algorithm.…”
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COMPUTER FORECASTING AND EXPERIMENTAL VALIDATION OF ANTIAGGREGATE PROPERTIES OF CETRINE, GLURENORM, MOEX AND DILTHIAZEM by Nikolay P. Votintsev

“…A new algorithm and 'Drug' database, permitting to predict new actions of familiar medical products, are developed and registered. …”
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Big Data Cleaning Based on Improved CLOF and Random Forest for Distribution Networks by Jie Liu, Yijia Cao, Yong Li, Yixiu Guo, Wei Deng

“…First, the Local Outlier Factor (LOF) algorithm based on DBSCAN clustering is used to detect outliers. …”
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Detection of Adulteration of Panax Notoginseng Powder by Terahertz Technology by Bin Li, Hai Yin, A-kun Yang, Ai-guo Ouyang

“…The model’s results show that the UVE-BPNN model is better; the classification accuracy of the prediction set of UVE-BPNN is 95%. Then, the least square support vector machine (LS-SVM) algorithm and partial least square (PLS) algorithm are used to establish the quantitative analysis model. …”
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