Sarbjit Singh

761 total citations · 1 hit paper
17 papers, 511 citations indexed

About

Sarbjit Singh is a scholar working on Management Science and Operations Research, Artificial Intelligence and Environmental Engineering. According to data from OpenAlex, Sarbjit Singh has authored 17 papers receiving a total of 511 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Management Science and Operations Research, 4 papers in Artificial Intelligence and 4 papers in Environmental Engineering. Recurrent topics in Sarbjit Singh's work include Air Quality Monitoring and Forecasting (4 papers), Energy Load and Power Forecasting (4 papers) and Forecasting Techniques and Applications (4 papers). Sarbjit Singh is often cited by papers focused on Air Quality Monitoring and Forecasting (4 papers), Energy Load and Power Forecasting (4 papers) and Forecasting Techniques and Applications (4 papers). Sarbjit Singh collaborates with scholars based in India, Japan and United States. Sarbjit Singh's co-authors include Kulwinder Singh Parmar, Jatinder Kumar, Jatinder Kaur, Santosh Shrivastava, P. Jeyanthi, Xing Fang, Jagdish Kumar, Piyush Gupta, Xiaohong Yuan and Arunabha Datta and has published in prestigious journals such as Green Chemistry, Environmental Science and Pollution Research and Chaos Solitons & Fractals.

In The Last Decade

Sarbjit Singh

15 papers receiving 486 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Autoregressive models in environmental forecasting time series: a theoretical and application review

2023 97 citations Jatinder Kaur, Kulwinder Singh Parmar et al. Environmental Science and Pollution Research profile →

Peers

Sarbjit Singh

MSOR

EEE

Ch. Mohan Sai Kumar India

K.E. ArunKumar India

Reza Paki Iran

Tanujit Chakraborty India

Jatinder Kumar India

Berna Uzun Cyprus

Huda M. Alshanbari Saudi Arabia

Mehmet Şahin Türkiye

Nadia Al‐Rousan Türkiye

Ch. Mohan Sai Kumar India

Sarbjit Singh

145 ×1.2

81 ×0.8

MSOR

97 ×1.1

70 ×0.8

138 ×1.7

EEE

Citations per year, relative to Sarbjit Singh Sarbjit Singh (= 1×) peers Ch. Mohan Sai Kumar

Countries citing papers authored by Sarbjit Singh

Since Specialization

Citations

This map shows the geographic impact of Sarbjit Singh's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sarbjit Singh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sarbjit Singh more than expected).

Fields of papers citing papers by Sarbjit Singh

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sarbjit Singh. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sarbjit Singh. The network helps show where Sarbjit Singh may publish in the future.

Co-authorship network of co-authors of Sarbjit Singh

This figure shows the co-authorship network connecting the top 25 collaborators of Sarbjit Singh. A scholar is included among the top collaborators of Sarbjit Singh based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sarbjit Singh. Sarbjit Singh is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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17 of 17 papers shown

Singh, Sarbjit, Kulwinder Singh Parmar, & Jatinder Kumar. (2024). Development of multi-forecasting model using Monte Carlo simulation coupled with wavelet denoising-ARIMA model. Mathematics and Computers in Simulation. 230. 517–540. 5 indexed citations

Kaur, Jatinder, Sarbjit Singh, & Kulwinder Singh Parmar. (2023). Forecasting of AQI (PM2.5) for the three most polluted cities in India during COVID-19 by hybrid Daubechies discrete wavelet decomposition and autoregressive (Db-DWD-ARIMA) model. Environmental Science and Pollution Research. 30(45). 101035–101052. 4 indexed citations

Kaur, Jatinder, Sarbjit Singh, Kulwinder Singh Parmar, & Kirti Soni. (2023). Development of a mathematical model to forecast black carbon concentration using ARIMA and soft computing. Arabian Journal of Geosciences. 16(4). 3 indexed citations

Kaur, Jatinder, Kulwinder Singh Parmar, & Sarbjit Singh. (2023). Autoregressive models in environmental forecasting time series: a theoretical and application review. Environmental Science and Pollution Research. 30(8). 19617–19641. 97 indexed citations breakdown →

Singh, Sarbjit, et al.. (2022). Comparison of hybrid machine learning model for the analysis of black carbon in air around the major coal mines of India. Neural Computing and Applications. 35(4). 3449–3468. 2 indexed citations

Singh, Sarbjit, et al.. (2021). Prediction of COVID-19 pervasiveness in six major affected states of India and two-stage variation with temperature. Air Quality Atmosphere & Health. 14(12). 2079–2090. 7 indexed citations

Singh, Sarbjit, Kulwinder Singh Parmar, & Jatinder Kumar. (2021). Soft computing model coupled with statistical models to estimate future of stock market. Neural Computing and Applications. 33(13). 7629–7647. 34 indexed citations

Shrivastava, Santosh, P. Jeyanthi, & Sarbjit Singh. (2020). Failure prediction of Indian Banks using SMOTE, Lasso regression, bagging and boosting. Cogent Economics & Finance. 8(1). 1729569–1729569. 58 indexed citations

Singh, Sarbjit, et al.. (2020). Study of ARIMA and least square support vector machine (LS-SVM) models for the prediction of SARS-CoV-2 confirmed cases in the most affected countries. Chaos Solitons & Fractals. 139. 110086–110086. 84 indexed citations

10.

Singh, Sarbjit, et al.. (2020). Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19. Chaos Solitons & Fractals. 135. 109866–109866. 121 indexed citations

11.

Singh, Sarbjit, et al.. (2019). Performance Analysis of Speaker Identification using Gaussian Mixture Model and Support Vector Machine. 1–5. 2 indexed citations

12.

Singh, Sarbjit. (2018). Solving Basic Inventory Models Using Excel. Theoretical Economics Letters. 8(11). 2095–2102.

13.

Singh, Sarbjit, et al.. (2016). Semi-Supervised Deep Neural Network for Network Intrusion Detection. DigitalCommons - Kennesaw State University (Kennesaw State University). 32 indexed citations

14.

Singh, Sarbjit. (2015). Note on Transportation Problem with New Method for Resolution of Degeneracy. 3(1). 26–36. 6 indexed citations

15.

Singh, Sarbjit. (2012). Note on Assignment Algorithm with Easy Method of Drawing Lines to Cover All Zeros. International Journal of Operations Research and Information Systems. 3(3). 87–97. 3 indexed citations

16.

Pendem, Chandrashekar, Piyush Gupta, Nisha Chaudhary, et al.. (2012). Aqueous phase reforming of glycerol to 1,2-propanediol over Pt-nanoparticles supported on hydrotalcite in the absence of hydrogen. Green Chemistry. 14(11). 3107–3107. 51 indexed citations

17.

Singh, Sarbjit & Nikolaos Papanikolopoulos. (1999). ADVANCES IN VISION-BASED DETECTION OF DRIVER FATIGUE. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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