Tayyaba Anees

1.1k total citations
35 papers, 648 citations indexed

About

Tayyaba Anees is a scholar working on Computer Networks and Communications, Information Systems and Artificial Intelligence. According to data from OpenAlex, Tayyaba Anees has authored 35 papers receiving a total of 648 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computer Networks and Communications, 9 papers in Information Systems and 8 papers in Artificial Intelligence. Recurrent topics in Tayyaba Anees's work include COVID-19 diagnosis using AI (6 papers), IoT and Edge/Fog Computing (5 papers) and Cloud Computing and Resource Management (5 papers). Tayyaba Anees is often cited by papers focused on COVID-19 diagnosis using AI (6 papers), IoT and Edge/Fog Computing (5 papers) and Cloud Computing and Resource Management (5 papers). Tayyaba Anees collaborates with scholars based in Pakistan, South Korea and Jordan. Tayyaba Anees's co-authors include Ahmad Naeem, Hassaan Malik, Rizwan Ali Naqvi, Seung Won Lee, Woong-Kee Loh, Muzammil Hussain, Muhammad Nabeel Asghar, Muhammad Faheem, Ahmad Sami Al‐Shamayleh and Wajeeha Khalil and has published in prestigious journals such as PLoS ONE, IEEE Access and Sensors.

In The Last Decade

Tayyaba Anees

33 papers receiving 616 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tayyaba Anees Pakistan 17 266 196 120 119 113 35 648
Hassaan Malik Pakistan 13 275 1.0× 221 1.1× 62 0.5× 132 1.1× 121 1.1× 36 701
Ibrahim Abunadi Saudi Arabia 18 363 1.4× 213 1.1× 115 1.0× 52 0.4× 157 1.4× 46 911
Halil Murat Ünver Türkiye 13 379 1.4× 360 1.8× 211 1.8× 167 1.4× 101 0.9× 35 934
Jan Harkes United States 11 336 1.3× 142 0.7× 311 2.6× 126 1.1× 178 1.6× 27 804
Håkon Kvale Stensland Norway 13 188 0.7× 143 0.7× 83 0.7× 170 1.4× 59 0.5× 51 669
Md Sipon Miah Bangladesh 15 359 1.3× 110 0.6× 131 1.1× 213 1.8× 26 0.2× 64 835
Azhar Imran Pakistan 21 447 1.7× 573 2.9× 177 1.5× 81 0.7× 163 1.4× 75 1.3k
Souad Larabi-Marie-Sainte Saudi Arabia 12 451 1.7× 85 0.4× 58 0.5× 120 1.0× 107 0.9× 38 797
Zhipeng Jia United States 8 359 1.3× 228 1.2× 176 1.5× 69 0.6× 163 1.4× 16 644
Areej Alasiry Saudi Arabia 14 173 0.7× 54 0.3× 69 0.6× 88 0.7× 67 0.6× 51 570

Countries citing papers authored by Tayyaba Anees

Since Specialization
Citations

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

Fields of papers citing papers by Tayyaba Anees

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tayyaba Anees

This figure shows the co-authorship network connecting the top 25 collaborators of Tayyaba Anees. A scholar is included among the top collaborators of Tayyaba Anees 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 Tayyaba Anees. Tayyaba Anees 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.
Naeem, Ahmad, et al.. (2024). SNC_Net: Skin Cancer Detection by Integrating Handcrafted and Deep Learning-Based Features Using Dermoscopy Images. Mathematics. 12(7). 1030–1030. 35 indexed citations
2.
Naeem, Ahmad & Tayyaba Anees. (2024). DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images. PLoS ONE. 19(3). e0297667–e0297667. 22 indexed citations
3.
Malik, Hassaan & Tayyaba Anees. (2024). Federated learning with deep convolutional neural networks for the detection of multiple chest diseases using chest x-rays. Multimedia Tools and Applications. 83(23). 63017–63045. 11 indexed citations
4.
Malik, Hassaan & Tayyaba Anees. (2024). Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds. PLoS ONE. 19(3). e0296352–e0296352. 17 indexed citations
5.
Anees, Tayyaba, et al.. (2024). TVET-SCon: A Unified Catalyst Framework for Enhancing Youth Employability. VFAST Transactions on Software Engineering. 12(4). 37–58.
6.
Anees, Tayyaba, et al.. (2023). Space-Air-Ground Integrated Network for Disaster Management: Systematic Literature Review. Applied Computational Intelligence and Soft Computing. 2023. 1–20. 10 indexed citations
7.
Khan, Ali Haider, et al.. (2023). Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images. Computers, materials & continua/Computers, materials & continua (Print). 76(1). 133–150. 1 indexed citations
8.
Malik, Hassaan, Tayyaba Anees, Ahmad Naeem, Rizwan Ali Naqvi, & Woong-Kee Loh. (2023). Blockchain-Federated and Deep-Learning-Based Ensembling of Capsule Network with Incremental Extreme Learning Machines for Classification of COVID-19 Using CT Scans. Bioengineering. 10(2). 203–203. 23 indexed citations
9.
Anees, Tayyaba, et al.. (2023). The Integration of WoT and Edge Computing: Issues and Challenges. Sustainability. 15(7). 5983–5983. 20 indexed citations
10.
Malik, Hassaan, et al.. (2023). Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images. Diagnostics. 13(17). 2772–2772. 8 indexed citations
11.
Malik, Hassaan, et al.. (2023). Blockchain and Internet of Things in smart cities and drug supply management: Open issues, opportunities, and future directions. Internet of Things. 23. 100860–100860. 47 indexed citations
12.
Naeem, Ahmad, Tayyaba Anees, Rizwan Ali Naqvi, & Woong-Kee Loh. (2022). A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis. Journal of Personalized Medicine. 12(2). 275–275. 66 indexed citations
13.
Naeem, Ahmad, et al.. (2022). SCDNet: A Deep Learning-Based Framework for the Multiclassification of Skin Cancer Using Dermoscopy Images. Sensors. 22(15). 5652–5652. 79 indexed citations
14.
Malik, Hassaan, et al.. (2022). CDC_Net: multi-classification convolutional neural network model for detection of COVID-19, pneumothorax, pneumonia, lung Cancer, and tuberculosis using chest X-rays. Multimedia Tools and Applications. 82(9). 13855–13880. 73 indexed citations
15.
Anees, Tayyaba, et al.. (2022). Keywords and Spatial Based Indexing for Searching the Things on Web. KSII Transactions on Internet and Information Systems. 16(5). 3 indexed citations
16.
Anees, Tayyaba, et al.. (2021). Fast Village Finder. 1–6. 1 indexed citations
17.
Anees, Tayyaba, et al.. (2020). Challenges and Solutions for Processing Real-Time Big Data Stream: A Systematic Literature Review. IEEE Access. 8. 119123–119143. 37 indexed citations
18.
Anees, Tayyaba, et al.. (2019). The Web of Things: Findability Taxonomy and Challenges. IEEE Access. 7. 185028–185041. 40 indexed citations
19.
Gupta, Saurabh Kumar, Kavassery Mahadevan Krishnamoorthy, S Sivasankaran, et al.. (2011). Percutaneous closure of patent ductus arteriosus in children: Immediate and short-term changes in left ventricular systolic and diastolic function. Annals of Pediatric Cardiology. 4(2). 139–139. 24 indexed citations
20.
Anees, Tayyaba, et al.. (2006). Severe hypoglycemia due to poorly differentiated hepatocellular carcinoma.. PubMed. 54. 413–5. 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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