Gas Concentration Prediction Based on the Measured Data of a Coal Mine Rescue Robot by Xiliang Ma, Hua Zhu

“…Experimental results show that a gray neural network optimized by the quantum genetic algorithm is more accurate for predicting the gas concentration. …”
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Short-term prediction of regional energy consumption by metaheuristic optimized deep learning models by Ngoc-Quang Nguyen, Phuong-Thao-Nguyen Nguyen, Quynh-Chau Truong

“…This study proposed a hybrid deep learning model, called I-CNN-JS, by incorporating a jellyfish search (JS) algorithm into an ImageNet-winning convolutional neural network (I-CNN) to predict week-ahead energy consumption. …”
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Mix design and performance prediction of EPS lightweight structural concrete based on orthogonal experimentation by Qianhui Zhang

“…A novel dataset was established and utilized in performance prediction using XGBoost, optimized with Seagull Optimization Algorithm (SOA), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO). …”
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Optimized Multivariate Adaptive Regression Splines for Predicting Crude Oil Demand in Saudi Arabia by Eman H. Alkhammash, Abdelmonaim Fakhry Kamel, Saud M. Al-Fattah, Ahmed M. Elshewey

“…This paper presents optimized linear regression with multivariate adaptive regression splines (LR-MARS) for predicting crude oil demand in Saudi Arabia based on social spider optimization (SSO) algorithm. …”
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STATE PREDICTION OF WIND TURBINE GENERATOR BASED ON K-CNN AND N-GRU (MT) by CHAI Tong, YUAN YiPing, MA JunYan, FAN PanPan

“…Then, to solve the problem of parameter optimization of the traditional GRU algorithm, the neural network architecture search was used to improve the GRU algorithm, and the N-GRU model was obtained. …”
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SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization. by Lei Li, Zhen Gao, Yu-Tian Wang, Ming-Wen Zhang, Jian-Cheng Ni, Chun-Hou Zheng, Yansen Su

“…In addition, the L2 regularization terms and similarity constraint terms were added to traditional Nonnegative Matrix Factorization algorithm to predict disease-related miRNAs. SCMFMDA achieved AUCs of 0.9675 and 0.9447 based on global Leave-one-out cross validation and five-fold cross validation, respectively. …”
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Application of Ensemble Learning and VISSIM in Intersection Traffic Flow Prediction and Signal Timing Optimization by Yutong Rou, Chao Liang, Zhizhan Lu

“…By integrating the predictions with the SARSA-A2C algorithm, a hybrid strategy for predictive signal timing optimization is implemented. …”
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Autonomous Driving Decision-Making Method Based on Spatial-Temporal Fusion Trajectory Prediction by Yutao Luo, Aining Sun, Jiawei Hong

“…The simulation results prove that the prediction algorithm can achieve the minimum error compared with the baseline trajectory prediction algorithm, and effectively improves the accuracy and reliability of the autopilot decision-making in various dynamic scenarios.…”
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Urban land use function prediction method based on RF and cellular automaton model by Wenjun Song, Min Ling

“…Moreover, the study also integrates random forest algorithm and cellular automaton model, and finally proposes a new urban land use function prediction method based on random forest algorithm and cellular automaton model. …”
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An incremental data-driven approach for carbon emission prediction and optimization of heat treatment processes by Qian Yi, Xin Wu, Junkang Zhuo, Congbo Li, Chuanjiang Li, Huajun Cao

“…The key process parameters affecting part performance and carbon emission were screened through mechanism analysis, and the incremental data were fused by the Elasticity Weight Consolidation (EWC) algorithm to establish an EWC-BPNN heat treatment carbon emission prediction model. …”
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Robust analysis of photovoltaic plants: A framework based on prediction uncertainties by machine learning by Seyyed Shahabaddin Hosseini Dehshiri, Bahar Firoozabadi

“…The gradient boosting regressor machine learning algorithm was predicted the PV plant for 10,000 different scenarios (R2 ∼ 98 %). …”
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From Rating Predictions to Reliable Recommendations in Collaborative Filtering: The Concept of Recommendation Reliability Classes by Dionisis Margaris, Costas Vassilakis, Dimitris Spiliotopoulos

“…In this paper, after performing a study on rating prediction confidence factors in collaborative filtering, (a) we introduce the concept of prediction reliability classes, (b) we rank these classes in relation to the utility of the rating predictions belonging to each class, and (c) we present a collaborative filtering recommendation algorithm which exploits these reliability classes for prediction formulation. …”
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Prediction and dynamic optimization of drilling performance based on the combination of mineral composition and operational factors by Xiong Xiuli, Li Qian, Liu Junhao, Jiang Jie

“…Through the established prediction function, a dynamic optimization algorithm combined with DOE (Design of Experiments) theory was also developed. …”
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Research on ocean buoy attitude prediction model based on multi-dimensional feature fusion by Yingjie Liu, Yingjie Liu, Chunlin Ning, Chunlin Ning, Chunlin Ning, Chunlin Ning, Qianran Zhang, Guozheng Yuan, Chao Li

“…Based on this, a Convolutional Neural Networks-Bidirectional Gated Recurrent Unit (CNN-BiGRU) buoy attitude prediction model is constructed. Experimental results demonstrate that the optimized prediction model, when combined with the feature selection algorithm, achieves a minimum prediction accuracy of 95.7%. …”
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Predicting Winter Wheat Grain Yield Using Fractional Green Canopy Cover (FGCC) by Vaughn Reed, Daryl B. Arnall, Bronc Finch, Joao Luis Bigatao Souza

“…Our objectives were to (1) quantify the relationship between NDVI and FGCC, (2) assess the potential for using FGCC values in place of NDVI values in the current OSU Yield Prediction Model, and (3) compare the performance of NDVI and FGCC-based yield prediction models from the collected dataset. …”
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Predicting effective drug combinations for cancer treatment using a graph-based approach by Qi Wang, Xiya Liu, Guiying Yan

“…Therefore, developing computational approaches to predict drug combinations has become increasingly important.In this paper, we developed the Random Walk with Restart for Drug Combination (RWRDC) model to predict effective drug combinations for cancer therapy. …”
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An incremental learning framework for pipeline weld crack damage identification and leakage rate prediction by Jing Huang, Zhifen Zhang, Yanlong Yu, Yongjie Li, Shuai Zhang, Rui Qin, Ji Xing, Wei Cheng, Guangrui Wen, Xuefeng Chen

“…To address the above problems, a novel framework called OILS-TCN for weld crack pattern recognition and leakage rate prediction is proposed. Firstly, the adaptive threshold optimization algorithm is introduced into the self-organizing incremental neural network to update and increase the crack leakage pattern. …”
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Prediction of SNR Based on SVR and Adaptive Transmission Power Method for Underwater Acoustic Communication by Jixing ZHENG, Yufan YUAN, Xiaoxiao ZHUO, Xuesong LU, Fengzhong QU, Yan WEI

“…The simulation results show that compared with the exponential smoothing and autoregressive integrated moving average model(ARIMA) methods, the SVR algorithm based on the linear kernel function has the best performance in predicting signal-to-noise ratios and the smallest prediction error on test data. …”
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Research into prediction and influential factors of circuit breaker closing time using BFGS-NN by Longcheng Dai, Jiaying Yu, Zhihui Huang, Hui Ni, Yifan Zhang, Junting Dou

“…On-site operational data were analyzed to build a circuit breaker action time database. The BFGS algorithm trained on these data generated a closing time prediction model, achieving rapid convergence and optimal fit during learning. …”
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Choice of machine learning models for predicting the development of psychological disorders in people with hypothireosis and hyperthireosis by Нурал Гулієв

“…The article solves the problem of choosing the best models for predicting the occurrence of psychological disorders in people with endocrinological problems. …”
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