Tausif Diwan

2.1k total citations · 1 hit paper
26 papers, 1.2k citations indexed

About

Tausif Diwan is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Neurology. According to data from OpenAlex, Tausif Diwan has authored 26 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 9 papers in Radiology, Nuclear Medicine and Imaging and 8 papers in Neurology. Recurrent topics in Tausif Diwan's work include Brain Tumor Detection and Classification (8 papers), AI in cancer detection (6 papers) and Sentiment Analysis and Opinion Mining (4 papers). Tausif Diwan is often cited by papers focused on Brain Tumor Detection and Classification (8 papers), AI in cancer detection (6 papers) and Sentiment Analysis and Opinion Mining (4 papers). Tausif Diwan collaborates with scholars based in India. Tausif Diwan's co-authors include Jitendra V. Tembhurne, Saket S. Chaturvedi, Hemprasad Yashwant Patil, Parul Sahare, Saroj K. Panda, O. G. Kakde, Tarun Saxena, Raghav Agarwal and Manish P. Kurhekar and has published in prestigious journals such as IEEE Access, Biomedical Signal Processing and Control and Multimedia Tools and Applications.

In The Last Decade

Tausif Diwan

21 papers receiving 1.1k citations

Hit Papers

align trajectories

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.

Object detection using YOLO: challenges, architectural successors, datasets and applications

2022 687 citations Tausif Diwan, Jitendra V. Tembhurne et al. Multimedia Tools and Applications profile →

Peers

Tausif Diwan

CVPR

AI

ONCOL

EPIDE

PS

Jitendra V. Tembhurne India

Loretta Ichim Romania

Syed Ahmad Chan Bukhari United States

Agnieszka Mikołajczyk Poland

Vatsala Anand India

Michał Grochowski Poland

Domenico D. Bloisi Italy

Muhammad Haroon Yousaf Pakistan

Tsubasa Hirakawa Japan

Serestina Viriri South Africa

Jitendra V. Tembhurne India

Tausif Diwan

432 ×1.2

CVPR

402 ×1.1

AI

238 ×1.0

ONCOL

136 ×1.0

EPIDE

121 ×1.1

PS

Citations per year, relative to Tausif Diwan Tausif Diwan (= 1×) peers Jitendra V. Tembhurne

Countries citing papers authored by Tausif Diwan

Since Specialization

Citations

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

Fields of papers citing papers by Tausif Diwan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tausif Diwan

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Diwan, Tausif, et al.. (2025). SH-StNN: prognostication of Alzheimer’s disease based on search and hunt-based stacked deep convolutional neural network. Cognitive Neurodynamics. 19(1). 167–167.

2.

Diwan, Tausif, et al.. (2025). Meta-Transformers: A Hybrid Approach for Medical Image Segmentation With U-Net and Meta-Learning. IEEE Access. 13. 64822–64831.

3.

Diwan, Tausif, et al.. (2024). An Empirical Survey on the Classification Strategies for Automated Alzheimer’s Disease. SN Computer Science. 5(8).

4.

Diwan, Tausif, et al.. (2024). Ensemble activation enabled deep classifier for Alzheimer's disease detection in the blockchain-enabled distributed edge environment. International Journal of Information Technology. 18(1). 543–560. 6 indexed citations

5.

Diwan, Tausif, et al.. (2024). Early detection of stroke disease using patients previous medical data instil with deep learning. Multimedia Tools and Applications. 84(16). 16853–16881. 2 indexed citations

6.

Panda, Saroj K., Tausif Diwan, & O. G. Kakde. (2024). Differently processed modality and appropriate model selection lead to richer representation of the multimodal input. International Journal of Information Technology. 16(7). 4505–4516. 2 indexed citations

7.

Tembhurne, Jitendra V., et al.. (2024). An Optimal Weighted Ensemble of 3D CNNs for Early Diagnosis of Alzheimer’s Disease. SN Computer Science. 5(2). 6 indexed citations

8.

Diwan, Tausif, et al.. (2024). Hybrid cuttle Fish-Grey wolf optimization tuned weighted ensemble classifier for Alzheimer’s disease classification. Biomedical Signal Processing and Control. 92. 106101–106101. 5 indexed citations

9.

Kurhekar, Manish P., et al.. (2024). Design of a Novel Enhanced Machine Learning Model for Early Prediction of Cerebral Stroke Early Prediction of Brain Stroke. International Journal of Computing and Digital Systems. 15(1). 1807–1821. 2 indexed citations

10.

Agarwal, Raghav, et al.. (2023). Personalized federated learning for the detection of COVID-19. Multimedia Tools and Applications. 83(10). 29067–29084. 1 indexed citations

11.

Tembhurne, Jitendra V., et al.. (2023). Plant disease detection using deep learning based Mobile application. Multimedia Tools and Applications. 82(18). 27365–27390. 15 indexed citations

12.

Tembhurne, Jitendra V., et al.. (2023). Skin cancer detection using ensemble of machine learning and deep learning techniques. Multimedia Tools and Applications. 82(18). 27501–27524. 80 indexed citations

13.

Tembhurne, Jitendra V., et al.. (2023). Improvised light weight deep CNN based U-Net for the semantic segmentation of lungs from chest X-rays. Results in Engineering. 17. 100929–100929. 37 indexed citations

14.

Diwan, Tausif, et al.. (2022). Object detection using YOLO: challenges, architectural successors, datasets and applications. Multimedia Tools and Applications. 82(6). 9243–9275. 687 indexed citations breakdown →

15.

Panda, Saroj K., Tausif Diwan, O. G. Kakde, & Jitendra V. Tembhurne. (2022). Improvised detection of deepfakes from visual inputs using light weight deep ensemble model. Multimedia Tools and Applications. 82(13). 20101–20118. 3 indexed citations

16.

Diwan, Tausif, et al.. (2022). Alzheimer disease classification using tawny flamingo based deep convolutional neural networks via federated learning. The Imaging Science Journal. 70(7). 459–472. 4 indexed citations

17.

Tembhurne, Jitendra V., et al.. (2022). Mc-DNN. International Journal on Semantic Web and Information Systems. 18(1). 1–20. 74 indexed citations

18.

Chaturvedi, Saket S., Jitendra V. Tembhurne, & Tausif Diwan. (2020). A multi-class skin Cancer classification using deep convolutional neural networks. Multimedia Tools and Applications. 79(39-40). 28477–28498. 190 indexed citations

19.

Diwan, Tausif, et al.. (2018). Survey on Tweet Segmentation and Sentiment Analysis. International Journal of Computer Sciences and Engineering. 6(1). 391–394.

20.

Diwan, Tausif, et al.. (2017). A Generalized Strategy for Parallelization of Non-Serial Polyadic Dynamic Programming on Multicore and Manycore. Advanced Science Letters. 23(4). 3802–3807. 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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