Muhammad Faheem

3.4k total citations · 3 hit papers
125 papers, 2.1k citations indexed

About

Muhammad Faheem is a scholar working on Electrical and Electronic Engineering, Computer Networks and Communications and Artificial Intelligence. According to data from OpenAlex, Muhammad Faheem has authored 125 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Electrical and Electronic Engineering, 39 papers in Computer Networks and Communications and 25 papers in Artificial Intelligence. Recurrent topics in Muhammad Faheem's work include IoT and Edge/Fog Computing (16 papers), Energy Efficient Wireless Sensor Networks (12 papers) and Energy Harvesting in Wireless Networks (11 papers). Muhammad Faheem is often cited by papers focused on IoT and Edge/Fog Computing (16 papers), Energy Efficient Wireless Sensor Networks (12 papers) and Energy Harvesting in Wireless Networks (11 papers). Muhammad Faheem collaborates with scholars based in Pakistan, Finland and Malaysia. Muhammad Faheem's co-authors include Vehbi Çağrı Güngör, Basit Raza, Rizwan Aslam Butt, Arfat Ahmad Khan, Gürkan Tuna, Md Asri Ngadi, Mahmoud Ahmad Al‐Khasawneh, Muhammad Anwar, Muhammad Shoaib Bhutta and Muhammad Waqar Ashraf and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and IEEE Access.

In The Last Decade

Muhammad Faheem

116 papers receiving 2.0k citations

Hit Papers

A Hybrid Convolutional Neural Network Model for Automatic... 2023 2026 2024 2025 2023 2024 2024 20 40 60

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Muhammad Faheem Pakistan 29 754 722 351 287 261 125 2.1k
Abdellah Chehri Canada 27 724 1.0× 1.2k 1.7× 490 1.4× 249 0.9× 157 0.6× 243 3.0k
Saif ul Islam Pakistan 31 1.2k 1.6× 1.1k 1.5× 362 1.0× 547 1.9× 128 0.5× 112 2.9k
Inam Ullah China 33 704 0.9× 905 1.3× 741 2.1× 276 1.0× 464 1.8× 147 3.2k
Lalit Kumar Awasthi India 19 796 1.1× 503 0.7× 779 2.2× 394 1.4× 157 0.6× 149 2.4k
Ahmed Abdelgawad United States 24 620 0.8× 529 0.7× 350 1.0× 252 0.9× 98 0.4× 118 1.9k
S. Neelakandan India 30 754 1.0× 519 0.7× 743 2.1× 448 1.6× 187 0.7× 75 2.5k
Rashid A. Saeed Sudan 26 1.2k 1.6× 1.2k 1.7× 401 1.1× 274 1.0× 91 0.3× 224 2.6k
Youseef Alotaibi Saudi Arabia 33 1.2k 1.6× 600 0.8× 507 1.4× 660 2.3× 210 0.8× 90 2.7k
Mohammad Hossein Anisi Malaysia 33 1.6k 2.1× 1.5k 2.1× 537 1.5× 273 1.0× 209 0.8× 117 3.2k
Yongjun Xu China 26 733 1.0× 702 1.0× 669 1.9× 236 0.8× 437 1.7× 141 2.5k

Countries citing papers authored by Muhammad Faheem

Since Specialization
Citations

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

Fields of papers citing papers by Muhammad Faheem

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Muhammad Faheem

This figure shows the co-authorship network connecting the top 25 collaborators of Muhammad Faheem. A scholar is included among the top collaborators of Muhammad Faheem 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 Muhammad Faheem. Muhammad Faheem 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.
Iqbal, Shahid, et al.. (2025). Dynamic smart contracts framework on Ethereum private blockchain for real estate management. The Journal of Engineering. 2025(1). 1 indexed citations
2.
Jaffar, M. Arfan, et al.. (2025). CMV2U‐Net: A U‐shaped network with edge‐weighted features for detecting and localizing image splicing. Journal of Forensic Sciences. 70(3). 1026–1043. 4 indexed citations
3.
Madni, Syed Hamid Hussain, et al.. (2025). Exploring optimizer efficiency for facial expression recognition with convolutional neural networks. The Journal of Engineering. 2025(1).
4.
Ahmed, Ishfaq, et al.. (2025). Predictive Insights from Machine Learning-Assisted Laser-Induced Breakdown Spectroscopy of Lanthanum Substituted Bismuth Ferrite. Arabian Journal for Science and Engineering. 50(18). 15187–15202. 4 indexed citations
5.
Asif, Muhammad, et al.. (2025). Capturing Discontinuities with Precision: A Numerical Exploration of 3D Telegraph Interface Models via Multi-Resolution Technique. Mathematics. 13(15). 2391–2391. 1 indexed citations
6.
Che, Yanbo, et al.. (2024). Machine learning autoencoder‐based parameters prediction for solar power generation systems in smart grid. IET Smart Grid. 7(3). 328–350. 23 indexed citations
7.
Raza, Basit, et al.. (2024). WaveSeg‐UNet model for overlapped nuclei segmentation from multi‐organ histopathology images. CAAI Transactions on Intelligence Technology. 10(1). 253–267. 5 indexed citations
8.
Raza, Muhammad Amir, et al.. (2024). Conventional and artificial intelligence based maximum power point tracking techniques for efficient solar power generation. Engineering Reports. 6(12). 15 indexed citations
9.
Faheem, Muhammad, et al.. (2024). Batch Normalization Free Rigorous Feature Flow Neural Network for Grocery Product Recognition. IEEE Access. 12. 68364–68381. 4 indexed citations
10.
Jaffar, M. Arfan, et al.. (2024). Revolutionizing Urdu Sentiment Analysis: Harnessing the Power of XLM-R and GPT-2. IEEE Access. 12. 99779–99793. 1 indexed citations
12.
Ojo, Stephen, et al.. (2024). Scalable Hybrid Switching-Driven Software Defined Networking Issue: From the Perspective of Reinforcement Learning Standpoint. IEEE Access. 12. 63334–63350. 4 indexed citations
13.
14.
Faheem, Muhammad, et al.. (2024). A Novel 1-Dimensional Cosine Chaotic Equation and Digital Image Encryption Technique. IEEE Access. 12. 118857–118874. 6 indexed citations
15.
Faheem, Muhammad, et al.. (2024). Evaluation of Efficiency Enhancement in Photovoltaic Panels via Integrated Thermoelectric Cooling and Power Generation. Energies. 17(11). 2590–2590. 9 indexed citations
16.
Rashid, Javed, et al.. (2023). Segmentation and classification of skin lesions using hybrid deep learning method in the Internet of Medical Things. Skin Research and Technology. 29(11). e13524–e13524. 38 indexed citations
17.
Bhutta, Muhammad Shoaib, et al.. (2023). Evaluation of Machine Learning Models for Smart Grid Parameters: Performance Analysis of ARIMA and Bi-LSTM. Sustainability. 15(11). 8555–8555. 27 indexed citations
18.
Raza, Basit, Ahmad Kamran Malik, Ahmad Raza Shahid, et al.. (2020). An Optimally Configured and Improved Deep Belief Network (OCI-DBN) Approach for Heart Disease Prediction Based on Ruzzo–Tompa and Stacked Genetic Algorithm. IEEE Access. 8. 65947–65958. 56 indexed citations
19.
Raza, Saleem, et al.. (2019). Industrial wireless sensor and actuator networks in industry 4.0: Exploring requirements, protocols, and challenges—A MAC survey. International Journal of Communication Systems. 32(15). 36 indexed citations
20.
Memon, Kamran Ali, Qi Zhang, Rizwan Aslam Butt, et al.. (2019). Traffic-Adaptive Inter Wavelength Load Balancing for TWDM PON. IEEE photonics journal. 12(1). 1–8. 10 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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