EDBT 2026 Demo / reviewers in the wild / expert
Jitendra V. Tembhurne
dblp:152/3783
· DBLP profile ↗
18ranked-venue papers
6as first author
17since 2021 · last 2025
0000-0002-1389-3456ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 14 · 4 first-author · 13 since 2021Databases, data management, data science and information retrieval · 2 · 2 first-author · 2 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Twitter sentiment analysis using ensemble of multi-channel model based on machine learning and deep learning techniques
Jitendra V. Tembhurne, Kirtan Lakhotia, Anant Agrawal |
Knowl. Inf. Syst. | 1 |
| 2025 | Early detection of stroke disease using patients previous medical data instil with deep learning
Tausif Diwan, Saurav M. Gajbhiye, Purva R. Goydani, Vedant R. Gannarpwar, Harshal R. Khandait, Jitendra V. Tembhurne, Parul Sahare |
Multim. Tools Appl. | 6 |
| 2024 | Improving coronary heart disease prediction with real-life dataset: a stacked generalization framework with maximum clinical attributes and SMOTE balancing for imbalanced data
Madhuri Dubey, Jitendra V. Tembhurne, Richa K. Makhijani |
Multim. Tools Appl. | 2 |
| 2024 | Deep learning based authentication schemes for smart devices in different modalities: progress, challenges, performance, datasets and future directions
Shailendra W. Shende, Jitendra V. Tembhurne, Nishat Afshan Ansari |
Multim. Tools Appl. | 2 |
| 2024 | Personalized federated learning for the detection of COVID-19
Dharwada Sesha Sriram, Aseem Ranjan, Vedant Ghuge, Naveen Rathore, Raghav Agarwal, Tausif Diwan, Jitendra V. Tembhurne |
Multim. Tools Appl. | 7 |
| 2024 | Localization in wireless sensor networks and wireless multimedia sensor networks using clustering techniques
Dipak W. Wajgi, Jitendra V. Tembhurne |
Multim. Tools Appl. | 2 |
| 2024 | Correction: Localization in wireless sensor networks and wireless multimedia sensor networks using clustering techniques
Dipak W. Wajgi, Jitendra V. Tembhurne |
Multim. Tools Appl. | 2 |
| 2023 | Object detection using YOLO: challenges, architectural successors, datasets and applications
Tausif Diwan, G. Anirudh, Jitendra V. Tembhurne |
Multim. Tools Appl. | 3 |
| 2023 | Improvised detection of deepfakes from visual inputs using light weight deep ensemble model
Saroj Kumar Panda, Tausif Diwan, Omprakash G. Kakde, Jitendra V. Tembhurne |
Multim. Tools Appl. | 4 |
| 2023 | Plant disease detection using deep learning based Mobile application
Jitendra V. Tembhurne, Saurav M. Gajbhiye, Vedant R. Gannarpwar, Harshal R. Khandait, Purva R. Goydani, Tausif Diwan |
Multim. Tools Appl. | 1 |
| 2023 | Skin cancer detection using ensemble of machine learning and deep learning techniques
Jitendra V. Tembhurne, Nachiketa Hebbar, Hemprasad Yashwant Patil, Tausif Diwan |
Multim. Tools Appl. | 1 |
| 2022 | Mc-DNN: Fake News Detection Using Multi-Channel Deep Neural NetworksabstractWith the advancement of technology, social media has become a major source of digital news due to its global exposure. This has led to an increase in spreading fake news and misinformation online. Humans cannot differentiate fake news from real news because they can be easily influenced. A lot of research work has been conducted for detecting fake news using Artificial Intelligence and Machine Learning. A large number of deep learning models and their architectural variants have been investigated and many websites are utilizing these models directly or indirectly to detect fake news. However, state-of-the-arts demonstrate the limited accuracy in distinguishing fake news from the original news. We propose a multi-channel deep learning model namely Mc-DNN, leveraging and processing the news headlines and news articles along different channels for differentiating fake or real news. We achieve the highest accuracy of 99.23% on ISOT Fake News Dataset and 94.68% on Fake News Data for Mc-DNN. Thus, we highly recommend the use of Mc-DNN for fake news detection. Jitendra V. Tembhurne, Md. Moin Almin, Tausif Diwan |
Int. J. Semantic Web Inf. Syst. | 1 |
| 2022 | Sentiment analysis: a convolutional neural networks perspective
Tausif Diwan, Jitendra V. Tembhurne |
Multim. Tools Appl. | 2 |
| 2021 | Sentiment analysis in textual, visual and multimodal inputs using recurrent neural networks
Jitendra V. Tembhurne, Tausif Diwan |
Multim. Tools Appl. | 1 |
| 2021 | BrC-MCDLM: breast Cancer detection using Multi-Channel deep learning model
Jitendra V. Tembhurne, Anupama Hazarika, Tausif Diwan |
Multim. Tools Appl. | 1 |
| 2021 | An effective mobile-healthcare emerging emergency medical system using conformable chaotic maps
Chandrashekhar Meshram, Rabha W. Ibrahim, Mohammad S. Obaidat, Balqies Sadoun, Sarita Gajbhiye Meshram, Jitendra V. Tembhurne |
Soft Comput. | 6 |
| 2021 | Provably secure lightweight client authentication scheme with anonymity for TMIS using chaotic hash function
Vishesh P. Gaikwad, Jitendra V. Tembhurne, Chandrashekhar Meshram, Cheng-Chi Lee |
J. Supercomput. | 2 |
| 2020 | A multi-class skin Cancer classification using deep convolutional neural networks
Saket S. Chaturvedi, Jitendra V. Tembhurne, Tausif Diwan |
Multim. Tools Appl. | 2 |