Shah Marjan

1.0k total citations · 2 hit papers
21 papers, 593 citations indexed

About

Shah Marjan is a scholar working on Information Systems, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Shah Marjan has authored 21 papers receiving a total of 593 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Information Systems, 7 papers in Artificial Intelligence and 6 papers in Signal Processing. Recurrent topics in Shah Marjan's work include Advanced Malware Detection Techniques (6 papers), Network Security and Intrusion Detection (5 papers) and Air Quality and Health Impacts (3 papers). Shah Marjan is often cited by papers focused on Advanced Malware Detection Techniques (6 papers), Network Security and Intrusion Detection (5 papers) and Air Quality and Health Impacts (3 papers). Shah Marjan collaborates with scholars based in Pakistan, China and Malaysia. Shah Marjan's co-authors include Uzair Aslam Bhatti, Hao Tang, Guilu Wu, Aamir Hussain, Sibghat Ullah Bazai, Ahmad Hasnain, Saad Aslam, Abdul Wahid, Syam M.S. and Hao Tang and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Cleaner Production and Expert Systems with Applications.

In The Last Decade

Shah Marjan

19 papers receiving 579 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.

Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence

2023 231 citations Uzair Aslam Bhatti, Hao Tang et al. International Journal of Intelligent Systems profile →
MFFCG – Multi feature fusion for hyperspectral image classification using graph attention network

2023 123 citations Uzair Aslam Bhatti, Shah Marjan et al. Expert Systems with Applications profile →

Peers

Shah Marjan

AI

CVPR

EE

CNC

IS

Sibghat Ullah Bazai Pakistan

Guilu Wu China

Rita Tse Macao

Raquel Martínez‐España Spain

Teerayut Horanont Thailand

Fengxiang Jin China

Xinying Xu China

Shaohua Xu China

Minyoung Kim South Korea

Himanshu Agrawal India

Sibghat Ullah Bazai Pakistan

Shah Marjan

88 ×0.6

AI

68 ×0.6

CVPR

65 ×0.8

EE

55 ×0.9

CNC

46 ×0.9

IS

Citations per year, relative to Shah Marjan Shah Marjan (= 1×) peers Sibghat Ullah Bazai

Countries citing papers authored by Shah Marjan

Since Specialization

Citations

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

Fields of papers citing papers by Shah Marjan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shah Marjan

This figure shows the co-authorship network connecting the top 25 collaborators of Shah Marjan. A scholar is included among the top collaborators of Shah Marjan 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 Shah Marjan. Shah Marjan 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:

20 of 20 papers shown

1.

Marjan, Shah, et al.. (2025). Sentiment Analysis of Balochi Text Using Deep Learning. 13(1). 190–200.

2.

Bazai, Sibghat Ullah, et al.. (2024). Urdu Sentiment Analysis Using Deep Learning. 1–7. 1 indexed citations

3.

Bhatti, Uzair Aslam, et al.. (2023). MFFCG – Multi feature fusion for hyperspectral image classification using graph attention network. Expert Systems with Applications. 229. 120496–120496. 123 indexed citations breakdown →

4.

Bhatti, Uzair Aslam, Shah Marjan, Abdul Wahid, et al.. (2023). The effects of socioeconomic factors on particulate matter concentration in China's: New evidence from spatial econometric model. Journal of Cleaner Production. 417. 137969–137969. 38 indexed citations

5.

Tang, Hao, Uzair Aslam Bhatti, Jingbing Li, et al.. (2023). A New Hybrid Forecasting Model Based on Dual Series Decomposition with Long‐Term Short‐Term Memory. International Journal of Intelligent Systems. 2023(1). 22 indexed citations

6.

Bazai, Sibghat Ullah, et al.. (2023). Forecasting Cryptocurrency Prices Using a Gated Recurrent Unit Neural Network. 1–6. 3 indexed citations

7.

Li, Qianmu, et al.. (2023). A Unified Learning Approach for Malicious Domain Name Detection. Axioms. 12(5). 458–458. 4 indexed citations

8.

Bhatti, Uzair Aslam, et al.. (2023). Deep Learning-Based Trees Disease Recognition and Classification Using Hyperspectral Data. Computers, materials & continua/Computers, materials & continua (Print). 77(1). 681–697. 24 indexed citations

9.

Haq, Shams Ul, Sibghat Ullah Bazai, Shah Marjan, et al.. (2023). Reseek-Arrhythmia: Empirical Evaluation of ResNet Architecture for Detection of Arrhythmia. Diagnostics. 13(18). 2867–2867. 2 indexed citations

10.

Marjan, Shah, et al.. (2023). A Comprehensive Survey on Sentiment Analysis Techniques. SHILAP Revista de lepidopterología. 14(6). 1288–1288. 11 indexed citations

11.

Bazai, Sibghat Ullah, et al.. (2023). A Review of Big Data Trends and Challenges in Healthcare. SHILAP Revista de lepidopterología. 14(6). 1320–1320. 6 indexed citations

12.

Bhatti, Uzair Aslam, Hao Tang, Guilu Wu, Shah Marjan, & Aamir Hussain. (2023). Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence. International Journal of Intelligent Systems. 2023(1). 231 indexed citations breakdown →

13.

Bazai, Sibghat Ullah, et al.. (2023). Systematic Literature Review: Evaluating Effects of Adversarial Attacks and Attack Generation Methods. 1–6. 4 indexed citations

14.

Bazai, Sibghat Ullah, et al.. (2023). Generative Adversarial Networks for Anomaly Detection: A Systematic Literature Review. 1–6.

15.

Bazai, Sibghat Ullah, et al.. (2023). A Review on the Security of IoT Networks: From Network Layer’s Perspective. IEEE Access. 11. 71073–71087. 39 indexed citations

16.

Hasnain, Ahmad, Yehua Sheng, Muhammad Zaffar Hashmi, et al.. (2022). Time Series Analysis and Forecasting of Air Pollutants Based on Prophet Forecasting Model in Jiangsu Province, China. Frontiers in Environmental Science. 10. 42 indexed citations

17.

Aamir, Muhammad, Mughair Aslam Bhatti, Sibghat Ullah Bazai, et al.. (2022). Predicting the Environmental Change of Carbon Emission Patterns in South Asia: A Deep Learning Approach Using BiLSTM. Atmosphere. 13(12). 2011–2011. 26 indexed citations

18.

Bazai, Sibghat Ullah, et al.. (2022). Cyber Security's Silver Bullet - A Systematic Literature Review of AI-Powered Security. 1–7. 1 indexed citations

19.

Bazai, Sibghat Ullah, et al.. (2022). Phishing and Intrusion Attacks: An Overview of Classification Mechanisms. 1–5. 1 indexed citations

20.

Bazai, Sibghat Ullah, et al.. (2021). Three-Tier Password Security Algorithm for Online Databases. 1–6. 1 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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