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Suraj Kumar Bhagat
Researcher at Ton Duc Thang University
Publications - 19
Citations - 586
Suraj Kumar Bhagat is an academic researcher from Ton Duc Thang University. The author has contributed to research in topics: Computer science & Support vector machine. The author has an hindex of 7, co-authored 15 publications receiving 181 citations.
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Development of artificial intelligence for modeling wastewater heavy metal removal: State of the art, application assessment and possible future research
TL;DR: In this review, each element of the predictive models and their corresponding treatment processes, including its pros and cons, are discussed thoroughly and several research directions, which could bridge the gap in the same domain are proposed and recommended on the basis of the identified research limitations.
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Heavy metal contamination prediction using ensemble model: Case study of Bay sedimentation, Australia.
TL;DR: The extreme gradient boosting (XGBoost) model is explored as a superior SuperLearning (SL) algorithms for Pb prediction using historical data at the Bramble and Deception Bay stations, Australia.
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Prediction of sediment heavy metal at the Australian Bays using newly developed hybrid artificial intelligence models.
Suraj Kumar Bhagat,Tiyasha Tiyasha,Salih Muhammad Awadh,Tran Minh Tung,Ali H. Jawad,Zaher Mundher Yaseen +5 more
TL;DR: The proposed hybrid AI models provided a reliable and robust computer aid technology for sediment Pb prediction that contribute to the best knowledge of environmental pollution monitoring and assessment.
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Manganese (Mn) removal prediction using extreme gradient model.
TL;DR: The XGBoost model validated against a diversity of data-driven models such as multilinear regression (MLR), support vector machine (SVM), and random forest (RF) and outperforms D2EHPA, EDTA, H2SO4, and NaCl predictors in order.
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Metaheuristic Optimization Algorithms Hybridized With Artificial Intelligence Model for Soil Temperature Prediction: Novel Model
Liu Penghui,Ahmed A. Ewees,Beste Hamiye Beyaztas,Chongchong Qi,Sinan Q. Salih,Nadhir Al-Ansari,Suraj Kumar Bhagat,Zaher Mundher Yaseen,Vijay P. Singh +8 more
TL;DR: The ANFIS-mSG model was demonstrated as an effective and simple hybrid artificial intelligence model for predicting soil temperature based on univariate air temperature scenario.