S. Shahab

1.0k total citations · 2 hit papers
6 papers, 652 citations indexed

About

S. Shahab is a scholar working on Environmental Engineering, Management Science and Operations Research and Economics and Econometrics. According to data from OpenAlex, S. Shahab has authored 6 papers receiving a total of 652 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Environmental Engineering, 2 papers in Management Science and Operations Research and 2 papers in Economics and Econometrics. Recurrent topics in S. Shahab's work include Hydrological Forecasting Using AI (2 papers), Stock Market Forecasting Methods (2 papers) and IoT and Edge/Fog Computing (1 paper). S. Shahab is often cited by papers focused on Hydrological Forecasting Using AI (2 papers), Stock Market Forecasting Methods (2 papers) and IoT and Edge/Fog Computing (1 paper). S. Shahab collaborates with scholars based in Vietnam, Slovakia and Iran. S. Shahab's co-authors include Amir Mosavi, Pooyan Nayyeri, Mojtaba Nabipour, Ely Salwana, Anichur Rahman, Mostofa Kamal Nasir, Behrouz Minaei‐Bidgoli, Ziaur Rahman, Alireza Baghban and Amin Bemani and has published in prestigious journals such as IEEE Access, Entropy and Mathematics.

In The Last Decade

S. Shahab

6 papers receiving 613 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.

Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis

2020 258 citations Mojtaba Nabipour, Pooyan Nayyeri et al. IEEE Access profile →
Deep Learning for Stock Market Prediction

2020 218 citations Mojtaba Nabipour, Pooyan Nayyeri et al. Entropy profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
S. Shahab Vietnam	6	351	189	115	104	85	6	652
Saman Haratizadeh Iran	9	358 1.0×	196 1.0×	128 1.1×	137 1.3×	86 1.0×	18	574
Lele Qin China	8	231 0.7×	159 0.8×	75 0.7×	104 1.0×	53 0.6×	24	557
Vinay Anand Tikkiwal India	13	351 1.0×	442 2.3×	91 0.8×	181 1.7×	86 1.0×	40	901
C. Narendra Babu India	12	285 0.8×	262 1.4×	84 0.7×	155 1.5×	31 0.4×	31	657
C. K. Jha India	9	200 0.6×	130 0.7×	50 0.4×	166 1.6×	22 0.3×	49	529
Cheng Zhao China	10	169 0.5×	128 0.7×	128 1.1×	74 0.7×	47 0.6×	33	493
Sarat Chandra Nayak India	14	300 0.9×	207 1.1×	82 0.7×	212 2.0×	26 0.3×	57	509
Baikunth Nath Australia	10	261 0.7×	148 0.8×	92 0.8×	194 1.9×	39 0.5×	36	821
Ratnadip Adhikari India	11	333 0.9×	273 1.4×	79 0.7×	161 1.5×	17 0.2×	14	592
Chih-Sheng Lin Taiwan	5	596 1.7×	356 1.9×	185 1.6×	167 1.6×	78 0.9×	13	814

Countries citing papers authored by S. Shahab

Since Specialization

Citations

This map shows the geographic impact of S. Shahab'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 S. Shahab with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites S. Shahab more than expected).

Fields of papers citing papers by S. Shahab

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by S. Shahab. 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 S. Shahab. The network helps show where S. Shahab may publish in the future.

Co-authorship network of co-authors of S. Shahab

This figure shows the co-authorship network connecting the top 25 collaborators of S. Shahab. A scholar is included among the top collaborators of S. Shahab 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 S. Shahab. S. Shahab is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

6 of 6 papers shown

1.

Bemani, Amin, Alireza Baghban, Amir Mosavi, & S. Shahab. (2020). Estimating CO2-Brine diffusivity using hybrid models of ANFIS and evolutionary algorithms. Engineering Applications of Computational Fluid Mechanics. 14(1). 818–834. 49 indexed citations

2.

Qasem, Sultan Noman, et al.. (2020). Dimension Reduction of Machine Learning-Based Forecasting Models Employing Principal Component Analysis. Mathematics. 8(8). 1233–1233. 12 indexed citations

3.

Nabipour, Mojtaba, et al.. (2020). Deep Learning for Stock Market Prediction. Entropy. 22(8). 840–840. 218 indexed citations breakdown →

4.

Riahi-Madvar, Hossien, et al.. (2020). Derivation of Optimized Equations for Estimation of Dispersion Coefficient in Natural Streams Using Hybridized ANN With PSO and CSO Algorithms. IEEE Access. 8. 156582–156599. 27 indexed citations

5.

Rahman, Anichur, Mostofa Kamal Nasir, Ziaur Rahman, et al.. (2020). DistBlockBuilding: A Distributed Blockchain-Based SDN-IoT Network for Smart Building Management. IEEE Access. 8. 140008–140018. 88 indexed citations

6.

Nabipour, Mojtaba, et al.. (2020). Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis. IEEE Access. 8. 150199–150212. 258 indexed citations breakdown →

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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