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 …
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 …
Streamflow prediction in mountainous region using new machine learning and data preprocessing methods: a case study
Accurate streamflow estimation is crucial for proper water management for irrigation,
hydropower, drinking and industrial purposes. The main aim of this study to adopt new data …
hydropower, drinking and industrial purposes. The main aim of this study to adopt new data …
Generalized robust loss functions for machine learning
Loss function is a critical component of machine learning. Some robust loss functions are
proposed to mitigate the adverse effects caused by noise. However, they still face many …
proposed to mitigate the adverse effects caused by noise. However, they still face many …
A systematic method of remaining useful life estimation based on physics-informed graph neural networks with multisensor data
Data-driven models, especially deep learning models, are proposed for remaining useful life
(RUL) estimation with multisensor signals. Various treatments to reduce data sensitivity …
(RUL) estimation with multisensor signals. Various treatments to reduce data sensitivity …
Density weighted twin support vector machines for binary class imbalance learning
BB Hazarika, D Gupta - Neural Processing Letters, 2022 - Springer
Usually the real-world (RW) datasets are imbalanced in nature, ie, there is a significant
difference between the number of negative and positive class samples in the datasets …
difference between the number of negative and positive class samples in the datasets …
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 …
Modeling suspended sediment load in a river using extreme learning machine and twin support vector regression with wavelet conjunction
Forecasting the sediment load in a river is difficult due to different parameters viz., heavy
rainfall and precipitation, tropical climate, transportation of sediment, and so on. The wavelet …
rainfall and precipitation, tropical climate, transportation of sediment, and so on. The wavelet …
An intuitionistic fuzzy random vector functional link classifier
Random vector functional link (RVFL) is a widely used powerful model for solving real-life
problems in classification and regression. However, the RVFL is not able to reduce the …
problems in classification and regression. However, the RVFL is not able to reduce the …