Sheraz Aslam

3.5k total citations · 1 hit paper
92 papers, 2.3k citations indexed

About

Sheraz Aslam is a scholar working on Electrical and Electronic Engineering, Control and Systems Engineering and Artificial Intelligence. According to data from OpenAlex, Sheraz Aslam has authored 92 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 50 papers in Electrical and Electronic Engineering, 27 papers in Control and Systems Engineering and 21 papers in Artificial Intelligence. Recurrent topics in Sheraz Aslam's work include Smart Grid Energy Management (28 papers), Microgrid Control and Optimization (21 papers) and IoT and Edge/Fog Computing (10 papers). Sheraz Aslam is often cited by papers focused on Smart Grid Energy Management (28 papers), Microgrid Control and Optimization (21 papers) and IoT and Edge/Fog Computing (10 papers). Sheraz Aslam collaborates with scholars based in Cyprus, Pakistan and Saudi Arabia. Sheraz Aslam's co-authors include Nadeem Javaid, Herodotos Herodotou, Syed Muhammad Mohsin, Nouman Ashraf, Michalis P. Michaelides, Khursheed Aurangzeb, Adia Khalid, Ahmad Taher Azar, Zahoor Ali Khan and Suheel Abdullah Malik and has published in prestigious journals such as SHILAP Revista de lepidopterología, Renewable and Sustainable Energy Reviews and Scientific Reports.

In The Last Decade

Sheraz Aslam

89 papers receiving 2.2k citations

Hit Papers

A survey on deep learning methods for power load and rene... 2021 2026 2022 2024 2021 100 200 300
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.

  1. A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids
    2021 387 citations Sheraz Aslam, Herodotos Herodotou et al. profile →

Peers

Sheraz Aslam
Mohammad Ghiasi Iran
M. Hadi Amini United States
Ming Jin United States
Ting Yang China
Khursheed Aurangzeb Saudi Arabia
Edoardo Patti Italy
Yuanzheng Li China
Abdollah Kavousi‐Fard Iran
Barry Hayes Ireland
Mohammad Ghiasi Iran
Sheraz Aslam
Citations per year, relative to Sheraz Aslam Sheraz Aslam (= 1×) peers Mohammad Ghiasi

Countries citing papers authored by Sheraz Aslam

Since Specialization
Citations

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

Fields of papers citing papers by Sheraz Aslam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sheraz Aslam

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

All Works

20 of 20 papers shown
1.
Aslam, Sheraz, et al.. (2025). Machine Learning-Based Predictive Maintenance at Smart Ports Using IoT Sensor Data. Sensors. 25(13). 3923–3923.
2.
Fouad, Mohamed Mostafa, et al.. (2025). Enhanced optimisation of MPLS network traffic using a novel adjustable Bat algorithm with loudness optimizer. Results in Engineering. 26. 104774–104774.
3.
Mushtaq, Muhammad Faheem, et al.. (2025). Time Series Analysis of Solar Power Generation Based on Machine Learning for Efficient Monitoring. Engineering Reports. 7(2). 2 indexed citations
4.
Aslam, Sheraz, Michalis P. Michaelides, & Herodotos Herodotou. (2024). Muli-Quay Combined Berth and Quay Crane Allocation Using the Cuckoo Search Algorithm. Ktisis at Cyprus University of Technology (Cyprus University of Technology). 220–227. 1 indexed citations
5.
Aslam, Sheraz, et al.. (2024). Enhancing Prediction Accuracy of Vessel Arrival Times Using Machine Learning. Journal of Marine Science and Engineering. 12(8). 1362–1362. 4 indexed citations
6.
Aslam, Sheraz, Michalis P. Michaelides, & Herodotos Herodotou. (2024). Optimizing Multi-Quay Combined Berth and Quay Crane Allocation Using Computational Intelligence. Journal of Marine Science and Engineering. 12(9). 1567–1567. 1 indexed citations
7.
Alkhalifah, Tamim, et al.. (2024). Evaluating Machine Unlearning: Applications, Approaches, and Accuracy. Engineering Reports. 7(1). 1 indexed citations
8.
9.
10.
Malik, Suheel Abdullah, et al.. (2023). Load Frequency Control and Automatic Voltage Regulation in Four-Area Interconnected Power Systems Using a Gradient-Based Optimizer. Energies. 16(5). 2086–2086. 31 indexed citations
11.
Aslam, Sheraz, Michalis P. Michaelides, & Herodotos Herodotou. (2023). Berth Allocation Considering Multiple Quays: A Practical Approach Using Cuckoo Search Optimization. Journal of Marine Science and Engineering. 11(7). 1280–1280. 13 indexed citations
12.
Ullah, Zahid, et al.. (2023). Optimizing Task Execution: The Impact of Dynamic Time Quantum and Priorities on Round Robin Scheduling. Future Internet. 15(3). 104–104. 5 indexed citations
13.
Ullah, Zahid, et al.. (2023). Deep learning-based meta-learner strategy for electricity theft detection. Frontiers in Energy Research. 11. 9 indexed citations
14.
Ahmed, Sheeraz, Zahoor Ali Khan, Syed Muhammad Mohsin, et al.. (2023). Effective and Efficient DDoS Attack Detection Using Deep Learning Algorithm, Multi-Layer Perceptron. Future Internet. 15(2). 76–76. 31 indexed citations
15.
Daraz, Amil, et al.. (2023). Modified FOPID Controller for Frequency Regulation of a Hybrid Interconnected System of Conventional and Renewable Energy Sources. Fractal and Fractional. 7(1). 89–89. 54 indexed citations
16.
Aslam, Sheraz, et al.. (2023). IoT for the Maritime Industry: Challenges and Emerging Applications. SHILAP Revista de lepidopterología. 35. 855–858. 4 indexed citations
17.
Aslam, Sheraz, et al.. (2022). A Hybrid Deep Learning-Based Model for Detection of Electricity Losses Using Big Data in Power Systems. Sustainability. 14(20). 13627–13627. 17 indexed citations
18.
Waheed, Abdul, Munam Ali Shah, Syed Muhammad Mohsin, et al.. (2022). A Comprehensive Review of Computing Paradigms, Enabling Computation Offloading and Task Execution in Vehicular Networks. IEEE Access. 10. 3580–3600. 49 indexed citations
19.
Ashfaq, Tehreem, Rabiya Khalid, Adamu Sani Yahaya, et al.. (2022). A Machine Learning and Blockchain Based Efficient Fraud Detection Mechanism. Sensors. 22(19). 7162–7162. 81 indexed citations
20.
Aslam, Sheraz, Michalis P. Michaelides, & Herodotos Herodotou. (2020). Internet of Ships: A Survey on Architectures, Emerging Applications, and Challenges. IEEE Internet of Things Journal. 7(10). 9714–9727. 174 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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