Construction and evaluation of yeast expression networks by database-guided predictions by Katharina Papsdorf, Siyuan Sima, Gerhard Richter, Klaus Richter

“…We then show the ability of our networks to accurately predict further differentially expressed genes. Including these predicted genes into the networks improves the network quality and allows quantifying the predictive strength of the networks based on a newly implemented scoring method. …”
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Research on COP Prediction Model of Chiller Based on PSO-SVR by Zhou Xuan, Cai Panpan, Lian Sizhen, Yan Junwei

“…The results shows that the prediction accuracy of SVR model based on PSO optimization algorithm is higher than that of BP neural network and the relative error is within 3%. …”
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Prediction of the Electricity Demand in the Market: An Application of Optimization and Machine Learning by Ahmed Majed Althahabi, Hassan Mohammed Abed, Raed Khalid, Abrar Ryadh, Ali Al Mansor, Kadhum Al-Majdi, Adil Abbas Alwan

“…In this study, the combination of Gray Wolf Optimization and Artificial neural networks (GWO-ANN) algorithm was applied to predict the long-term electricity demand in Iraq, considering the nonlinear trend and uncertainties in the variables affecting it. …”
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Prediction of complex organic compounds activity with artificial neural networks. by E. V. Burljaeva, P. A. Ushakov

“…The analysis of neural networks applicability for complex organic compounds activity prediction is provided. The regulation algorithm is offered to improve the prediction properties of the networks.…”
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System load trend prediction method based on IF-EMD-LSTM by Jing Yu, Feng Ding, Chenghao Guo, Yabin Wang

“…Second, in order to further improve the prediction accuracy, the empirical modal decomposition algorithm is used to decompose the input data into intrinsic mode function (IMF) components of different frequencies. …”
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Prediction of Students’ Performance Based on the Hybrid IDA-SVR Model by Huan Xu

“…The aim of this study is to propose a novel intelligent approach to predict students’ performance using support vector regression (SVR) optimized by an improved duel algorithm (IDA). …”
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Prediction of Low-Temperature Rheological Properties of SBS Modified Asphalt by Qian Chen, Chaohui Wang, Liang Song

“…The extreme learning machine (ELM) algorithm optimized by genetic algorithm (GA) was used to quickly predict the low-temperature rheological properties of styrenic block copolymer (SBS) modified asphalt through the properties of the raw materials. …”
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Congestion control strategy of VANET channel based on load prediction by Yang Ge, Zhu Yonghao

“…Finally the obtained load prediction value is compared with the preset standard value, and the power control algorithm is used to adjust the transmission power according to the comparison result to avoid channel congestion in advance. …”
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A New Hybrid Model for Underwater Acoustic Signal Prediction by Guohui Li, Wanni Chang, Hong Yang

“…Support vector regression (SVR) is used to predict the high-frequency subsequence. In addition, an artificial bee colony (ABC) algorithm is used to optimize model performance by adjusting the parameters of SVR. …”
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Coal and gas outburst prediction based on data augmentation and neuroevolution. by Wenbing Shi, Ji Huang, Gaoming Yang, Shuzhi Su, Shexiang Jiang

“…This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. …”
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A comprehensive survey of imbalanced learning methods for bankruptcy prediction by Tuong Le

“…To give an overview of imbalanced learning methods for bankruptcy prediction, this study first reviews several state‐of‐the‐art approaches for handling this problem in bankruptcy prediction, including an oversampling‐based framework, a cost‐sensitive method (the CBoost algorithm), a combination of resampling techniques and a cost‐sensitive framework, and an ensemble‐based model (the XGBS algorithm). …”
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Euclidean Distance-Based Weighted Prediction for Merge Mode in HEVC by Hongwei Guo, Xiangsuo Fan, Lei Min

“…To address this problem, the paper proposes a Euclidean distance-based weighted prediction algorithm as an additional candidate in the merge mode. …”
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A hybrid VMD-LSTM-SVR model for landslide prediction by Nianhong Wang, Meijun Wang, Jun Zhang

“…This study employs the Long Short-Term Memory (LSTM) neural network and Support Vector Regression (SVR), combined with the Variational Mode Decomposition (VMD) algorithm, to construct predictive models. Initially, the VMD algorithm decomposes the landslide displacement time series into trend, periodic, and stochastic components. …”
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DBO-DELM Method for Predicting Rolling Forces in Cold Rolling by LI Xiaoyang, PIAO Chunhui, WANG Xuelei, ZHANG Mingzhi

“…Aiming at the problems of many assumptions, large computational errors and poor generalisation performance of the traditional rolling force prediction model, a cold rolling force prediction model (DBO-DELM) using the dung beetle optimizer algorithm (DBO) to optimise the deep extreme learning machine (DELM) is proposed. …”
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A novel hybrid model for predicting the bearing capacity of piles by Li Tao, Xinhua Xue

“…The main objective of this study is to propose a hybrid model coupling least squares support vector machine (LSSVM) with an improved particle swarm optimization (IPSO) algorithm for the prediction of bearing capacity of piles. …”
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PREDICTION OF UNFAVORABLE OUTCOMES OF PREGNANCY BASED ON BIOCHEMICAL SCREENING IN TRIMESTER by I. O. Makarov, E. V. Yudina, E. I. Borovkova, I. V. Martynova, E. I. Kirpikova

“…The international and national approaches to the algorithm of carrying out of prenatal screening and conceptualization about prediction of pregnancy complications based on results of biochemical screening of serum concentrations of β-hCG and PAPP-A, are reviewed…”
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Machine learning frameworks to accurately predict coke reactivity index by Ayat Hussein Adhab, Morug Salih Mahdi, Krunal Vaghela, Anupam Yadav, Jayaprakash B, Mayank Kundlas, Ankur Srivastava, Jayant Jagtap, Aseel Salah Mansoor, Usama Kadem Radi, Nasr Saadoun Abd, Samim Sherzod

“…In this research, several machine learning predictive models based on extra trees, decision tree, support vector machine, random forest, multilayer perceptron artificial neural network, K-nearest neighbors, convolutional neural network, ensemble learning, and adaptive boosting using a dataset gathered from a coke plant are developed to predict CRI. …”
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Predicting carbohydrate quality in a global database of packaged foods by Eric Antoine Scuccimarra, Alexandre Arnaud, Marie Tassy, Marie Tassy, Kim-Anne Lê, Fabio Mainardi

“…Knowledge of specific carbohydrate in packaged food, such as added and free sugars, could help further investigate this link, however this information is generally not available.ObjectiveTo develop an algorithm to predict the content of free sugars in a global database of packaged foods and beverages; and test the applicability of the algorithm to assess carbohydrate quality in packaged food products from different countries and monitor the evolution over time. …”
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TBESO-BP: an improved regression model for predicting subclinical mastitis by Kexin Han, Yongqiang Dai, Huan Liu, Junjie Hu, Leilei Liu, Zhihui Wang, Liping Wei

“…In comparison to six alternative models, the TBESO-BP model demonstrates superior accuracy and lower error values.DiscussionThe TBESO-BP model emerges as a precise tool for predicting subclinical mastitis in dairy cows. The TBESO algorithm notably enhances the efficacy of the BP neural network in regression prediction, ensuring elevated computational efficiency and practicality post-improvement.…”
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Study on AdaBoost-based link quality prediction mechanism by Jian SHU, Man-lan LIU, Wei ZHENG

“…The link quality was vulnerable to the complexity environment in wireless sensor network.Obtaining link quality information in advance could reduce energy consumption of nodes.After analyzing the existing link quality prediction methods,AdaBoost-based link quality prediction mechanism was put forward.Link quality samples in deferent scenarios were collected.Density-based unsupervised clustering algorithm was employed to classify training samples into deferent link quality levels.The AdaBoost with SVM-based component classifiers was adopted to build link quality prediction mechanism.Experimental results show that the proposed mechanism has better prediction precision.…”
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