SOURAV KUMAR PUROHIT

@srmap.edu.in

Assistant Professor, Department of Computer Science and Engineering
SRM University-AP, Andhra Pradesh

SOURAV KUMAR PUROHIT


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https://researchid.co/skp1996

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Computer Engineering, Computer Science, Computer Science Applications
7

Scopus Publications

170

Scholar Citations

4

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • Accurate Air Quality Index Prediction Using Variational Mode Decomposition and Stacked Ensemble Learning
    Sushree Subhaprada Pradhan, Kedar Kumar Dora, Sourav Kumar Purohit
    Communications in Computer and Information Science, 2026
  • Decomposition-based hybrid methods employing statistical, machine learning, and deep learning models for crude oil price forecasting
    Sourav Kumar Purohit, Sibarama Panigrahi
    Neural Computing and Applications, 2025
  • Crude Oil Price Forecasting Using Hybridization of Optimized Deep Learning and Shallow Machine Learning Models
    Sourav Kumar Purohit, Sibarama Panigrahi, Aditya Narayan Jena
    Communications in Computer and Information Science, 2025
  • Novel deterministic and probabilistic forecasting methods for crude oil price employing optimized deep learning, statistical and hybrid models
    Sourav Kumar Purohit, Sibarama Panigrahi
    Information Sciences, 2024
  • Forecasting Crude Oil Prices: A Machine Learning Perspective
    Sourav Kumar Purohit, Sibarama Panigrahi
    Communications in Computer and Information Science, 2024
  • Study and development of hybrid and ensemble forecasting models for air quality index forecasting
    Sushree Subhaprada Pradhan, Sibarama Panigrahi, Sourav Kumar Purohit, Jatindra Kumar Dash
    Expert Systems, 2023
    In this paper, a viable, robust, and highly accurate additive hybrid model employing autoregressive fractionally integrated moving average (ARFIMA) and support vector machine (SVM) with functionally expanded inputs (Additive‐ARFIMA‐SVM) is presented for forecasting the air quality index (AQI). Additionally, thirteen additive and multiplicative hybrid models are introduced. Several alternatives in feature engineering employing functional expansion of inputs are incorporated to boost the performance of hybrid models. Furthermore, a gradient whale optimization algorithm with group best leader strategy (GWOA‐GBL) based meta‐heuristic algorithm is proposed. The missing values are imputed and a variable weight ensemble forecasting model is developed using the proposed GWOA‐GBL algorithm. To evaluate the effectiveness of the proposed Additive‐ARFIMA‐SVM forecasting model with functionally expanded inputs, comparisons are made with sixteen machine learning models, including long short‐term memory (LSTM), five statistical models, seventeen hybrid models, and ten variable weight ensemble models. Extensive statistical analyses are carried out on the obtained results considering four accuracy measures that show the statistical supremacy of the proposed Additive‐ARFIMA‐SVM model and GWOA‐GBL algorithm in predicting the AQI time series. The proposed Additive‐ARFIMA‐SVM model with functionally expanded inputs improves the AQI forecasting performance by 16.34% than autoregressive integrated moving average, 14.47% than ARFIMA, 33.96% than XGBoost, 43.47% than SVM, 49.39% than LSTM, 8.64% than Multiplicative‐ARIMA‐SVM model considering symmetric mean absolute percentage error. The proposed Additive‐ARFIMA‐SVM model is so efficient and reliable that it can be applied to forecast other time series like stock price, electricity load, crude oil price, sunspot number, stream flow, flood, drought etc.
  • Time Series Forecasting of Price of Agricultural Products Using Hybrid Methods
    Sourav Kumar Purohit, Sibarama Panigrahi, Prabira Kumar Sethy, Santi Kumari Behera
    Applied Artificial Intelligence, 2021
    Accurate prediction of crop prices assists farmers to decide the best time to sell their produce so as to get maximum benefit and assists Government for post-harvest storage and management of the produce so as to stabilize the price volatility throughout the year. At the same time, pricing of crop depends on various factors including the amount of cultivation, demand of consumers, climate, etc. Hence, the prediction of crop prices is a challenging and important problem. Inspired from this, in this study, we have proposed two additive hybrid methods (Additive-ETS-SVM, Additive-ETS-LSTM) and five multiplicative hybrid methods (Multiplicative-ETS-ANN, Multiplicative-ETS-SVM, Multiplicative-ETS-LSTM, Multiplicative-ARIMA-SVM, Multiplicative-ARIMA-LSTM) to predict the monthly retail and wholesale price of three most commonly used vegetable crops of India, namely, tomato, onion, and potato (TOP). The obtained results are compared with two most promising statistical models, three leading machine learning models and five hybrid methods existing in the literature. Extensive statistical analyses of simulation results considering mean absolute error (MAE), symmetric mean absolute percentage error (SMAPE), and root mean square error (RMSE) confirm the superiority of the hybrid methods in predicting the TOP prices.

RECENT SCHOLAR PUBLICATIONS

  • Accurate Air Quality Index Prediction Using Variational Mode Decomposition and Stacked Ensemble Learning
    SS Pradhan, KK Dora, SK Purohit
    International Conference on Computing, Communication and Learning, 207-222 , 2025
    2025.0
  • Decomposition-based hybrid methods employing statistical, machine learning, and deep learning models for crude oil price forecasting
    SK Purohit, S Panigrahi
    Neural Computing and Applications 37 (18), 12565-12610 , 2025
    2025.0
    Citations: 4
  • Ranking Optimised Statistical Models for Time Series Forecasting of Crude Oil Price
    SK Purohit, S Panigrahi
    Computing, Communication and Intelligence, 177-181 , 2025
    2025.0
    Citations: 1
  • Crude oil price forecasting using hybridization of optimized deep learning and shallow machine learning models
    SK Purohit, S Panigrahi, AN Jena
    International Conference on Computing, Communication and Learning, 3-16 , 2024
    2024.0
    Citations: 2
  • Novel deterministic and probabilistic forecasting methods for crude oil price employing optimized deep learning, statistical and hybrid models
    SK Purohit, S Panigrahi
    Information Sciences 658, 120021 , 2024
    2024.0
    Citations: 37
  • Study and development of hybrid and ensemble forecasting models for air quality index forecasting
    SS Pradhan, S Panigrahi, SK Purohit, JK Dash
    Expert Systems 40 (10), e13449 , 2023
    2023.0
    Citations: 8
  • Forecasting crude oil prices: a machine learning perspective
    SK Purohit, S Panigrahi
    International Conference on Computing, Communication and Learning, 15-26 , 2023
    2023.0
    Citations: 5
  • Time series forecasting of price of agricultural products using hybrid methods
    SK Purohit, S Panigrahi, PK Sethy, SK Behera
    Applied Artificial Intelligence 35 (15), 1388-1406 , 2021
    2021.0
    Citations: 113
  • A Novel Series-Parallel Hybrid Method Employing Fusion of Deep Features for Air Quality Index Forecasting
    SS Pradhan, S Panigrahi, SK Purohit
    Available at SSRN 5275908 , 0

MOST CITED SCHOLAR PUBLICATIONS

  • Time series forecasting of price of agricultural products using hybrid methods
    SK Purohit, S Panigrahi, PK Sethy, SK Behera
    Applied Artificial Intelligence 35 (15), 1388-1406 , 2021
    2021.0
    Citations: 113
  • Novel deterministic and probabilistic forecasting methods for crude oil price employing optimized deep learning, statistical and hybrid models
    SK Purohit, S Panigrahi
    Information Sciences 658, 120021 , 2024
    2024.0
    Citations: 37
  • Study and development of hybrid and ensemble forecasting models for air quality index forecasting
    SS Pradhan, S Panigrahi, SK Purohit, JK Dash
    Expert Systems 40 (10), e13449 , 2023
    2023.0
    Citations: 8
  • Forecasting crude oil prices: a machine learning perspective
    SK Purohit, S Panigrahi
    International Conference on Computing, Communication and Learning, 15-26 , 2023
    2023.0
    Citations: 5
  • Decomposition-based hybrid methods employing statistical, machine learning, and deep learning models for crude oil price forecasting
    SK Purohit, S Panigrahi
    Neural Computing and Applications 37 (18), 12565-12610 , 2025
    2025.0
    Citations: 4
  • Crude oil price forecasting using hybridization of optimized deep learning and shallow machine learning models
    SK Purohit, S Panigrahi, AN Jena
    International Conference on Computing, Communication and Learning, 3-16 , 2024
    2024.0
    Citations: 2
  • Ranking Optimised Statistical Models for Time Series Forecasting of Crude Oil Price
    SK Purohit, S Panigrahi
    Computing, Communication and Intelligence, 177-181 , 2025
    2025.0
    Citations: 1
  • Accurate Air Quality Index Prediction Using Variational Mode Decomposition and Stacked Ensemble Learning
    SS Pradhan, KK Dora, SK Purohit
    International Conference on Computing, Communication and Learning, 207-222 , 2025
    2025.0
  • A Novel Series-Parallel Hybrid Method Employing Fusion of Deep Features for Air Quality Index Forecasting
    SS Pradhan, S Panigrahi, SK Purohit
    Available at SSRN 5275908 , 0