Akbar Telikani

923 total citations
23 papers, 582 citations indexed

About

Akbar Telikani is a scholar working on Artificial Intelligence, Computer Networks and Communications and Information Systems. According to data from OpenAlex, Akbar Telikani has authored 23 papers receiving a total of 582 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 8 papers in Computer Networks and Communications and 5 papers in Information Systems. Recurrent topics in Akbar Telikani's work include Privacy-Preserving Technologies in Data (6 papers), Imbalanced Data Classification Techniques (6 papers) and Data Mining Algorithms and Applications (5 papers). Akbar Telikani is often cited by papers focused on Privacy-Preserving Technologies in Data (6 papers), Imbalanced Data Classification Techniques (6 papers) and Data Mining Algorithms and Applications (5 papers). Akbar Telikani collaborates with scholars based in Australia, Iran and China. Akbar Telikani's co-authors include Amir H. Gandomi, Asadollah Shahbahrami, Jun Shen, Amirhessam Tahmassebi, Wolfgang Banzhaf, Bo Du, Jie Yang, Peng Wang, Kim‐Kwang Raymond Choo and Mahdi Fahmideh and has published in prestigious journals such as SHILAP Revista de lepidopterología, Applied Energy and IEEE Communications Surveys & Tutorials.

In The Last Decade

Akbar Telikani

21 papers receiving 555 citations

Peers

Akbar Telikani

CNC

CSE

Daniele Apiletti Italy

Fabian Keller Germany

Fernando Paredes Chile

Leonora Bianchi Switzerland

Ioan Dumitrache Romania

Junqi Zhang China

Ranjith Kumar A India

Yunlong Ma China

Jingling Yuan China

Daniele Apiletti Italy

Akbar Telikani

184 ×0.6

119 ×0.9

CNC

112 ×0.8

38 ×0.6

CSE

52 ×0.8

Citations per year, relative to Akbar Telikani Akbar Telikani (= 1×) peers Daniele Apiletti

Countries citing papers authored by Akbar Telikani

Since Specialization

Citations

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

Fields of papers citing papers by Akbar Telikani

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Akbar Telikani

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

All Works

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20 of 20 papers shown

Telikani, Akbar, Bo Du, Fendy Santoso, et al.. (2025). Autonomous Aerial Vehicles-Aided Intelligent Transportation Systems: Vision, Challenges, and Opportunities. IEEE Communications Surveys & Tutorials. 27(6). 3772–3819. 6 indexed citations

Telikani, Akbar, et al.. (2025). Fire front path planning and tracking control of Uncrewed Aerial Vehicles using deep reinforcement learning. Robotics and Autonomous Systems. 193. 105076–105076. 1 indexed citations

Telikani, Akbar, Jun Shen, Bo Du, Mahdi Fahmideh, & Jun Yan. (2025). A Periodic Adversarial Threat Model for Deep Neural Networks in Aerial Vehicle Detection. IEEE Internet of Things Journal. 12(14). 26361–26370.

Telikani, Akbar, et al.. (2024). Machine Learning for UAV-Aided ITS: A Review With Comparative Study. IEEE Transactions on Intelligent Transportation Systems. 25(11). 15388–15406. 21 indexed citations

Zhai, Yanlong, et al.. (2024). Adaptive Period Control for Communication Efficient and Fast Convergent Federated Learning. IEEE Transactions on Mobile Computing. 23(12). 12572–12586. 2 indexed citations

Santoso, Fendy, et al.. (2024). Evaluating Energy Consumption Prediction Models of a Quadcopter Unmanned Aerial Vehicle. 1–7.

Santoso, Fendy, et al.. (2024). Smart Verification of Unmanned Aerial Vehicle GPS Geolocation via Received Signal Strength Indicators. 1–6. 2 indexed citations

Telikani, Akbar, Asadollah Shahbahrami, Jun Shen, Georgi Gaydadjiev, & Jerry Chun‐Wei Lin. (2023). An edge-aided parallel evolutionary privacy-preserving algorithm for Internet of Things. Internet of Things. 23. 100831–100831. 5 indexed citations

Telikani, Akbar, et al.. (2022). Pumps-as-Turbines’ (PaTs) performance prediction improvement using evolutionary artificial neural networks. Applied Energy. 330. 120316–120316. 16 indexed citations

10.

Wei, Wei, Jun Shen, Akbar Telikani, Mahdi Fahmideh, & Wei Gao. (2022). Feasibility Analysis of Data Transmission in Partially Damaged IoT Networks of Vehicles. IEEE Transactions on Intelligent Transportation Systems. 24(4). 4577–4588. 6 indexed citations

11.

Telikani, Akbar, Jun Shen, Jie Yang, & Peng Wang. (2022). Industrial IoT Intrusion Detection via Evolutionary Cost-Sensitive Learning and Fog Computing. IEEE Internet of Things Journal. 9(22). 23260–23271. 40 indexed citations

12.

Wu, Qiang, Jianqing Wu, Jun Shen, et al.. (2022). Distributed agent-based deep reinforcement learning for large scale traffic signal control. Knowledge-Based Systems. 241. 108304–108304. 52 indexed citations

13.

Telikani, Akbar, Jun Shen, Peng Wang, & Jie Yang. (2022). A privacy-aware data sharing framework for Internet of Things through edge computing platform. Research Online (University of Wollongong). 114–122. 2 indexed citations

14.

Telikani, Akbar, Asadollah Shahbahrami, & Amir H. Gandomi. (2021). High-performance implementation of evolutionary privacy-preserving algorithm for big data using GPU platform. Information Sciences. 579. 251–265. 8 indexed citations

15.

Telikani, Akbar, Amir H. Gandomi, Kim‐Kwang Raymond Choo, & Jun Shen. (2021). A Cost-Sensitive Deep Learning-Based Approach for Network Traffic Classification. IEEE Transactions on Network and Service Management. 19(1). 661–670. 39 indexed citations

16.

Telikani, Akbar, Amir H. Gandomi, & Asadollah Shahbahrami. (2020). A survey of evolutionary computation for association rule mining. Information Sciences. 524. 318–352. 135 indexed citations

17.

Telikani, Akbar, et al.. (2019). Privacy-preserving in association rule mining using an improved discrete binary artificial bee colony. Expert Systems with Applications. 144. 113097–113097. 33 indexed citations

18.

Telikani, Akbar & Amir H. Gandomi. (2019). Cost-sensitive stacked auto-encoders for intrusion detection in the Internet of Things. Internet of Things. 14. 100122–100122. 34 indexed citations

19.

Telikani, Akbar & Asadollah Shahbahrami. (2017). Optimizing association rule hiding using combination of border and heuristic approaches. Applied Intelligence. 47(2). 544–557. 19 indexed citations

20.

Telikani, Akbar, et al.. (2015). Data sanitization in association rule mining based on impact factor. SHILAP Revista de lepidopterología. 3(2). 6 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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