Abstract Every country must have an accurate and efficient forecasting model to avoid manage the epidemic. This paper suggests upgrade one of evolutionary algorithms inspired by nature, Barnacle Mating Optimizer (BMO). First, exploration phase original BMO is enhanced enforcing replacing sperm cast equation through Levy flight. Then, Least Square Support Vector Machine (LSSVM) partnered with im...

Abstract Monthly runoff forecasting has always been a key problem in water resources management. As data-driven method, the least square support vector machine (LSSVM) method investigated by numerous studies forecasting. However, selecting appropriate parameters for LSSVM is to obtaining satisfactory model performance. In this study, we propose hybrid monthly forecasting, VMD-SSA-LSSVM short, w...

For the limitation of traditional information fusion technology in the mine gas safety class predicition, an intelligent algorithm is proposed in which Genetic Algorithms is adopted to optimize the parameters of the least squares support vector machine and establishes a multi-sensor information fusion model GA-LSSVM which overcomes the subjectivity and blindness on parameters selection, and thu...

A new method based on least square support vector machine (LSSVM) combined with FastICA is proposed to extract the fetal electrocardiogram (FECG) from the abdominal signals of a pregnant woman. Firstly, the LSSVM is applied to estimate the maternal electrocardiogram (MECG) component in the multiplex abdominal signals. Then the optimal estimation of multiplex noise-added FECG is obtained by remo...

Machine learning algorithms are extensively used to reduce the complexity of applied problems in various fields, including energy. Accurate prediction performance water alternating gas (WAG) injection as an enhanced oil recovery (EOR) process is great importance optimal management hydrocarbon resources. In current work, a hybrid mathematical model proposed for near-immiscible WAG process. We us...

In order to realize real-time and precise monitoring of the tool wear in milling process, this paper presents a predictive model based on stacked multilayer denoising autoencoders (SMDAE) technique, particle swarm optimization with an adaptive learning strategy (PSO-ALS), least squares support vector machine (LSSVM). Cutting force vibration information are adopted as signals. Three steps make u...

In power generation industries, boilers are required to be operated under a range of different conditions accommodate demands for fuel randomness and energy fluctuation. Reliable prediction the combustion operation condition is crucial an in-depth understanding boiler performance maintaining high efficiency. However, it difficult establish accurate model based on traditional data-driven methods...

In this study, a novel least square support vector machine (LSSVM) model integrated with gradient-based optimizer (GBO) algorithm is introduced for the assessment of water quality (WQ) parameters. For purpose, three stations, including Ahvaz, Armand, and Gotvand in Karun river basin, have been selected to electrical conductivity (EC) total dissolved solids (TDS). First, prove superiority LSSVM-...

Numerous studies show that it is reasonable and effective to apply decomposition technology deal with the complex carbon price series. However, existing research ignores residual term containing information after applying single technique. Considering demand for higher accuracy of series prediction following path, this paper proposes a new hybrid model VMD-CEEMDAN-LSSVM-LSTM, which combines qua...