An overview on twin support vector regression
H Huang, X Wei, Y Zhou - Neurocomputing, 2022 - Elsevier
Twin support vector regression (TSVR) is a useful extension of traditional support vector
regression (SVR). As a new regression model, the basic idea of TSVR is generating a pair of …
regression (SVR). As a new regression model, the basic idea of TSVR is generating a pair of …
Artificial intelligence for suspended sediment load prediction: a review
The estimation of sediment yield concentration is crucial for the development of stream
ventures, watershed management, toxins estimation, soil disintegration, floods, and so on. In …
ventures, watershed management, toxins estimation, soil disintegration, floods, and so on. In …
Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization
Accurate runoff estimation is crucial for optimal reservoir operation and irrigation purposes.
In this study, a novel hybrid method is proposed for monthly runoff prediction in Mangla …
In this study, a novel hybrid method is proposed for monthly runoff prediction in Mangla …
Modelling and forecasting of COVID-19 spread using wavelet-coupled random vector functional link networks
BB Hazarika, D Gupta - Applied Soft Computing, 2020 - Elsevier
Researchers around the world are applying various prediction models for COVID-19 to
make informed decisions and impose appropriate control measures. Because of a high …
make informed decisions and impose appropriate control measures. Because of a high …
Density-weighted support vector machines for binary class imbalance learning
BB Hazarika, D Gupta - Neural Computing and Applications, 2021 - Springer
In real-world binary classification problems, the entirety of samples belonging to each class
varies. These types of problems where the majority class is notably bigger than the minority …
varies. These types of problems where the majority class is notably bigger than the minority …
Sensor data-driven structural damage detection based on deep convolutional neural networks and continuous wavelet transform
Structural damage detection is of very importance to improve reliability and safety of civil
structures. A novel sensor data-driven structural damage detection method is proposed in …
structures. A novel sensor data-driven structural damage detection method is proposed in …
Suspended sediment load prediction modelling based on artificial intelligence methods: The tropical region as a case study
The impact of the suspended sediment load (SSL) on environmental health, agricultural
operations, and water resources planning, is significant. The deposit of SSL restricts the …
operations, and water resources planning, is significant. The deposit of SSL restricts the …
Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms
High loads of suspended sediments in rivers are known to cause detrimental effects to
potable water sources, river water quality, irrigation activities, and dam or reservoir …
potable water sources, river water quality, irrigation activities, and dam or reservoir …
Comparison of Bayesian, k-Nearest Neighbor and Gaussian process regression methods for quantifying uncertainty of suspended sediment concentration prediction
Suspended sediment transport in river system is a complex process influenced by many
factors that their interactions lead to nonlinear and high scatter of concentration-discharge …
factors that their interactions lead to nonlinear and high scatter of concentration-discharge …
Random vector functional link with ε-insensitive Huber loss function for biomedical data classification
BB Hazarika, D Gupta - Computer methods and programs in biomedicine, 2022 - Elsevier
Background and objective Biomedical data classification has been a trending topic among
researchers during the last decade. Biomedical datasets may contain several features …
researchers during the last decade. Biomedical datasets may contain several features …