Time series data aggregation algorithm with synchronous prediction for WBAN by Ru-yan WANG, Mei-ling ZHAI, Da-peng WU

“…Due to the nonlinearity and nonstationarity of the physiological data sensed by WBAN,data aggregation cannot be effectively achieved according to the time-domain trend of data.Therefore,a novel time series data aggregation algorithm with synchronous prediction was proposed.By preprocessing the original sensing data with the multi-resolution analysis,the inherent characteristics of the physiological data can be obtained to establish a light-weight synchronous prediction model at both the sensor and sink.Numerical results show that the proposed aggregation algorithm can achieve a favorable prediction precision and a low energy consumption rate by eliminating the in-network data redundancy.…”
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FATIGUE CRACK GROWTH PREDICTION BASED ON IPSO-PF ALGORITHM by JIN Ting, WANG Xiaolei, LIU Yu, YUAN Jianming

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Quality Prediction of Web Services Based on a Covering Algorithm by Ying Jin, Guangming Cui, Yiwen Zhang

“…In this paper, we propose UIQPCA, a novel approach for hybrid User and Item-based Quality Prediction with Covering Algorithm. UIQPCA integrates information of both users and Web services on the basis of users’ ideas on the quality of coinvoked Web services. …”
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Computerised Method of Multiparameter Optimisation of Predictive Control Algorithms for Asynchronous Electric Drives by Grygorii Diachenko, Serhii Semenov, Katarzyna Marczak, Gernot Schullerus, Ivan Laktionov

“…This paper proposes a computerised method for the multiparameter optimisation of predictive control algorithms for asynchronous electric drives. …”
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A hybrid machine learning algorithm approach to predictive maintenance tasks: A comparison with machine learning algorithms by Jorge Paredes, Danilo Chávez, Ramiro Isa-Jara, Diego Vargas

“…This data can provide valuable insights into the behavior of a specific machine, enabling optimization or the prediction of potential malfunctions. Supervised machine learning algorithms are capable of predicting the remaining useful life (RUL) of a machine. …”
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INTERPRETABLE PREDICTIVE MODEL OF NETWORK INTRUSION USING SEVERAL MACHINE LEARNING ALGORITHMS by Muhammad Ahsan, Arif Khoirul Anam, Erdi Julian, Andi Indra Jaya

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Predicting Online Learners’ Performance Through Ontologies: A Systematic Literature Review by Safa Ridha Albo Abdullah, Ahmed Al-Azawei

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Aquaculture Prediction Model Based on Improved Water Quality Parameter Data Prediction Algorithm under the Background of Big Data by Yuan Jiang, Fei Yan

“…Considering the complex relationship between dissolved oxygen and water quality, combined with principal component analysis, a PCA-BP (principal component analysis back propagation) water quality prediction model was proposed. The parameters of PCA-BP water quality prediction model were optimized by genetic algorithm, the threshold and weight of BP neural network were determined, and an improved PCA-BP water quality prediction model was constructed. …”
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Postpartum depression risk prediction using explainable machine learning algorithms by Xudong Huang, Lifeng Zhang, Chenyang Zhang, Jing Li, Chenyang Li

“…Feature selection was performed using LASSO regression and the Boruta algorithm. Eight machine learning algorithms were then employed to construct the prediction models. …”
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Prediction of dialysis adequacy using data-driven machine learning algorithms by Yi-Chen Liu, Ji-Ping Qing, Rong Li, Juan Chang, Li-Xia Xu

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Long Prediction Horizon FCS-MPC for Power Converters and Drives by Eduardo Zafra, Sergio Vazquez, Tobias Geyer, Ricardo P. Aguilera, Leopoldo G. Franquelo

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Dual Coordinate Descent Algorithms for Efficient Large Margin Structured Prediction by Ming-Wei Chang, Wen-tau Yih

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Predicting agricultural drought in central Europe by using machine learning algorithms by Endre Harsányi

“…The findings of this research promote RF as a reliable algorithm for predicting SPEI droughts.…”
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Prediction of growth and feed efficiency in mink using machine learning algorithms by A. Shirzadifar, G. Manafiazar, P. Davoudi, D. Do, G. Hu, Y. Miar

“…The XGB algorithm can be an accurate algorithm to predict the ADG, FCR, and RFI values without measuring costly daily feed intake. …”
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Hybrid machine learning algorithms accurately predict marine ecological communities by Luciana Erika Yaginuma, Luciana Erika Yaginuma, Fabiane Gallucci, Danilo Cândido Vieira, Paula Foltran Gheller, Simone Brito de Jesus, Thais Navajas Corbisier, Gustavo Fonseca

“…Predicting ecological communities is highly challenging but necessary to establish effective conservation and monitoring programs. …”
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Live Weight Prediction in Norduz Sheep Using Machine Learning Algorithms by Cihan Çakmakçı

“…There were no differences between the means of actual and predicted LWs by machine learning models. The fact that the models generalized well on the testing data sets indicates that machine learning algorithms have valid predictive patterns and are effective methods in LW weight of Norduz sheep. …”
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Multiple Machine Learning Algorithms-based NBA Team Playoffs Prediction by Yeung Manho

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PREDICTION OF TELECOM SERVICES CONSUMERS CHURN BY USING MACHINE LEARNING ALGORITHMS by Edin Osmanbegović, Anel Džinić, Mirza Suljić

“…One of those areas is the prediction and prevention of consumer churn. There are two basic types of consumer churn, complete churn and partial churn. …”
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Phase diagram construction and prediction method based on machine learning algorithms by Shengkun Xi, Jiahui Li, Longke Bao, Rongpei Shi, Haijun Zhang, Xiaoyu Chong, Zhou Li, Cuiping Wang, Xingjun Liu

“…Meanwhile, the CALPHAD method has accumulated abundant high-quality phase diagram data who would be the perfect training data for the machine learning algorithms. In the present work, a phase diagram prediction method which integrates machine learning algorithms with CALPHAD descriptors is proposed. …”
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A Comparative Analysis of Machine Learning Algorithms in Energy Poverty Prediction by Elpida Kalfountzou, Lefkothea Papada, Christos Tourkolias, Sevastianos Mirasgedis, Dimitris Kaliampakos, Dimitris Damigos

“…The present paper adds new insights to the existing literature by exploring the capacity of ML algorithms to successfully predict energy poverty, as defined by different indicators, for the case of the “Urban Region of Athens” in Greece. …”
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