Tariq Sadad

1.4k total citations · 1 hit paper
32 papers, 929 citations indexed

About

Tariq Sadad is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, Tariq Sadad has authored 32 papers receiving a total of 929 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 9 papers in Radiology, Nuclear Medicine and Imaging and 8 papers in Computer Vision and Pattern Recognition. Recurrent topics in Tariq Sadad's work include AI in cancer detection (10 papers), Brain Tumor Detection and Classification (8 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). Tariq Sadad is often cited by papers focused on AI in cancer detection (10 papers), Brain Tumor Detection and Classification (8 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). Tariq Sadad collaborates with scholars based in Pakistan, Saudi Arabia and China. Tariq Sadad's co-authors include Tanzila Saba, Amjad Rehman, Saeed Ali Bahaj, Asim Munir, Hoshang Kolivand, Ayyaz Hussain, Usman Tariq, Rashid Abbasi, Noor Ayesha and Zahid Mehmood and has published in prestigious journals such as PLoS ONE, IEEE Access and Sensors.

In The Last Decade

Tariq Sadad

30 papers receiving 872 citations

Hit Papers

Anomaly-based intrusion detection system for IoT networks... 2022 2026 2023 2024 2022 50 100 150 200 250
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. Anomaly-based intrusion detection system for IoT networks through deep learning model
    2022 259 citations Tanzila Saba, Amjad Rehman et al. Computers & Electrical Engineering profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tariq Sadad Pakistan 15 482 369 207 195 181 32 929
C. Narmatha Saudi Arabia 11 194 0.4× 132 0.4× 163 0.8× 58 0.3× 68 0.4× 35 506
Salah Ud Din China 14 475 1.0× 91 0.2× 125 0.6× 95 0.5× 118 0.7× 25 905
El-Sayed M. El-Horbaty Egypt 13 541 1.1× 110 0.3× 521 2.5× 101 0.5× 214 1.2× 54 1.1k
J. Dinesh Peter India 18 319 0.7× 140 0.4× 272 1.3× 34 0.2× 316 1.7× 66 821
Ajaz Hussain Mir India 14 158 0.3× 253 0.7× 383 1.9× 107 0.5× 73 0.4× 90 873
Lianhua Chi Australia 16 373 0.8× 133 0.4× 136 0.7× 96 0.5× 47 0.3× 57 669
Muhammad Nazir Pakistan 21 369 0.8× 44 0.1× 452 2.2× 83 0.4× 212 1.2× 53 1.0k
Khaled Mohamad Almustafa Saudi Arabia 14 198 0.4× 184 0.5× 78 0.4× 42 0.2× 77 0.4× 56 660
Kashif Zafar Pakistan 14 210 0.4× 111 0.3× 152 0.7× 53 0.3× 51 0.3× 46 506
B. Surendiran India 13 307 0.6× 147 0.4× 117 0.6× 87 0.4× 71 0.4× 54 540

Countries citing papers authored by Tariq Sadad

Since Specialization
Citations

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

Fields of papers citing papers by Tariq Sadad

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tariq Sadad

This figure shows the co-authorship network connecting the top 25 collaborators of Tariq Sadad. A scholar is included among the top collaborators of Tariq Sadad 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 Tariq Sadad. Tariq Sadad 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.
Alamri, Faten S., et al.. (2025). Mango Disease Detection Using Fused Vision Transformer with ConvNeXt Architecture. Computers, materials & continua/Computers, materials & continua (Print). 83(1). 1023–1039. 2 indexed citations
2.
3.
Safran, Mejdl, et al.. (2024). Domain adaptive learning for multi realm sentiment classification on big data. PLoS ONE. 19(4). e0297028–e0297028.
4.
Sadad, Tariq, Mejdl Safran, Inayat Khan, et al.. (2023). Efficient Classification of ECG Images Using a Lightweight CNN with Attention Module and IoT. Sensors. 23(18). 7697–7697. 15 indexed citations
5.
Sadad, Tariq, et al.. (2023). Stress Monitoring Using Machine Learning, IoT and Wearable Sensors. Sensors. 23(21). 8875–8875. 27 indexed citations
6.
Khan, Inayat, et al.. (2023). NPR-LBN: next point of interest recommendation using large bipartite networks with edge and cloud computing. Journal of Cloud Computing Advances Systems and Applications. 12(1). 5 indexed citations
7.
Sadad, Tariq, et al.. (2023). Classification of hyperspectral images using fusion of CNN and MiniGCN with SVM. International Journal of Digital Earth. 16(1). 3601–3617. 4 indexed citations
8.
9.
Al-kahtani, Mohammed S., et al.. (2023). Intrusion Detection in the Internet of Things Using Fusion of GRU-LSTM Deep Learning Model. Intelligent Automation & Soft Computing. 37(2). 2279–2290. 21 indexed citations
10.
Sadad, Tariq, et al.. (2023). Classification of Highly Divergent Viruses from DNA/RNA Sequence Using Transformer-Based Models. Biomedicines. 11(5). 1323–1323. 10 indexed citations
11.
Sadad, Tariq, Syed Ahmad Chan Bukhari, Asim Munir, et al.. (2022). Detection of Cardiovascular Disease Based on PPG Signals Using Machine Learning with Cloud Computing. Computational Intelligence and Neuroscience. 2022. 1–11. 38 indexed citations
12.
13.
Alyami, Jaber, et al.. (2022). Automatic skin lesions detection from images through microscopic hybrid features set and machine learning classifiers. Microscopy Research and Technique. 85(11). 3600–3607. 10 indexed citations
14.
15.
Saba, Tanzila, Amjad Rehman, Tariq Sadad, Hoshang Kolivand, & Saeed Ali Bahaj. (2022). Anomaly-based intrusion detection system for IoT networks through deep learning model. Computers & Electrical Engineering. 99. 107810–107810. 259 indexed citations breakdown →
16.
Batool, Aisha, Muhammad Wasif Nisar, Jamal Hussain Shah, Amjad Rehman, & Tariq Sadad. (2021). iELMNet: An Application for Traffic Sign Recognition using CNN and ELM. 132–137. 5 indexed citations
17.
Mehmood, Gulzar, Muhammad Sohail Khan, Abdul Waheed, et al.. (2021). An Efficient and Secure Session Key Management Scheme in Wireless Sensor Network. Complexity. 2021(1). 22 indexed citations
18.
Sadad, Tariq, Amjad Rehman, Asim Munir, et al.. (2021). Brain tumor detection and multi‐classification using advanced deep learning techniques. Microscopy Research and Technique. 84(6). 1296–1308. 159 indexed citations
19.
Sadad, Tariq, Ayyaz Hussain, Asim Munir, et al.. (2020). Identification of Breast Malignancy by Marker-Controlled Watershed Transformation and Hybrid Feature Set for Healthcare. Applied Sciences. 10(6). 1900–1900. 45 indexed citations
20.
Rehman, Amjad, Hikmat Yar, Noor Ayesha, & Tariq Sadad. (2020). Dermoscopy Cancer Detection and Classification using Geometric Feature based on Resource Constraints Device (Jetson Nano). 412–417. 2 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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