Manuscript Number : IJSRST52310421

An Evolutionary Fake News Detection Based on Tropical Convolutional Neural Networks (TCNNs) Approach

Authors(6) :-Dr. Vishal Verma, Apoorva Dwivedi, Kajal, Prof. (Dr.) Devendra Agarwal, Dr. Fokrul Alom Mazarbhuiya, Dr. Yusuf Perwej

In general, the characteristics of false news are difficult to distinguish from those of legitimate news. Even if it is wrong, people can make money by spreading false information. A long time ago, there were fake news stories, including the one about "Bat-men on the moon" in 1835. A mechanism for fact-checking statements must be put in place, particularly those that garner thousands of views and likes before being refuted and proven false by reputable sources. Many machine learning algorithms have been used to precisely categorize and identify fake news. In this experiment, an ML classifier was employed to distinguish between fake and real news. In this study, we present a Tropical Convolutional Neural Networks (TCNNs) model-based false news identification system. Convolutional neural networks (CNNs), Gradient Boost, long short-term memory (LSTMs), Random Forest, Decision Tree (DT), Ada Boost, and attention mechanisms are just a few of the cutting-edge techniques that are compared in our study. Furthermore, because tropical convolution operators are fundamentally nonlinear operators, we anticipate that TCNNs will be better at nonlinear fitting than traditional CNN. Our analysis leads us to the conclusion that the Tropical Convolutional Neural Networks (TCNNs) model with attention mechanism has the maximum accuracy of 98.93%. The findings demonstrate that TCNN can outperform regular convolutional neural network (CNN) layers in terms of expressive capability.

Authors and Affiliations

Dr. Vishal Verma
Assistant Professor, Department of Computer Application & Sciences, School of Management Sciences (SMS), Lucknow, Uttar Pradesh, India
Apoorva Dwivedi
Assistant Professor, Department of Computer Science & Engineering, Invertis University, Bareilly, Uttar Pradesh, India
Kajal
Assistant Professor, Department of Computer Science & Engineering, M.G. Institute of Management & Technology, Lucknow
Prof. (Dr.) Devendra Agarwal
Dean (Academics), Goel Institute of Technology & Management, Lucknow, Uttar Pradesh, India
Dr. Fokrul Alom Mazarbhuiya
Associate Professor, Department of Mathematics, School of Fundamental and Applied Sciences, Assam Don Bosco University, Guwahati, Assam
Dr. Yusuf Perwej
Professor, Department of Computer Science & Engineering, Ambalika Institute of Management & Technology, Lucknow, Uttar Pradesh, India

Fake Profile, Tropical Convolutional Neural Networks (TCNNs), Detection, Fake News, Classification, LIAR Dataset, Machine Learning.

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Publication Details

Published in : Volume 10 | Issue 4 | July-August 2023
Date of Publication : 2023-08-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 266-286
Manuscript Number : IJSRST52310421
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Dr. Vishal Verma, Apoorva Dwivedi, Kajal, Prof. (Dr.) Devendra Agarwal, Dr. Fokrul Alom Mazarbhuiya, Dr. Yusuf Perwej, " An Evolutionary Fake News Detection Based on Tropical Convolutional Neural Networks (TCNNs) Approach", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 10, Issue 4, pp.266-286, July-August-2023. Available at doi : https://doi.org/10.32628/IJSRST52310421    
Journal URL : https://ijsrst.com/IJSRST52310421
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  • Prof. Kameswara Rao Poranki, Dr. Yusuf Perwej, Dr. Asif Perwej,” The Level of Customer Satisfaction related to GSM in India “, TIJ's Research Journal of Science & IT Management – RJSITM, International Journal's-Research Journal of Science & IT Management of Singapore, ISSN: 2251-1563, Singapore, in www.theinternationaljournal.org as RJSSM, Volume 04, Number: 03, Pages 29-36 , 2015
  • Guess A, Nagler J, Tucker J. Less than you think: Prevalence and predictors of fake news dissemination on Facebook. Science Advances. 2019;5: eaau4586. pmid:30662946
  • Nikhat Akhtar, Nazia Tabassum, Dr. Asif Perwej, Dr. Yusuf Perwej,“ Data Analytics and Visualization Using Tableau Utilitarian for COVID-19 (Coronavirus)”, Global Journal of Engineering and Technology Advances (GJETA), ISSN : 2582-5003, Volume 3, Issue 2, Pages 28-50, May 2020, DOI: 10.30574/gjeta.2020.3.2.0029
  • Kaliyar, R.K.; Goswami, A.; Narang, P. EchoFakeD: Improving fake news detection in social media with an efficient deep neural network. Neural Comput. Appl., 33, 8597–8613, 2021
  • Golbeck, J.; Mauriello, M.; Auxier, B.; Bhanushali, K.H.; Bonk, C.; Bouzaghrane, M.A.; Buntain, C.; Chanduka, R.; Cheakalos, P.; Everett, J.B.; et al. Fake News vs Satire: A Dataset and Analysis; WebSci ’18; Association for Computing Machinery: New York, NY, USA, pp. 17–21, 2018
  • Y. Perwej, Dr. Shaikh Abdul Hannan, Firoj Parwej, Nikhat Akhtar, “A Posteriori Perusal of Mobile Computing”, International Journal of Computer Applications Technology and Research (IJCATR), ATS (Association of Technology and Science), India, ISSN 2319–8656 (Online), Volume 3, Issue 9, Pages 569 - 578, September 2014, DOI: 10.7753/IJCATR0309.1008
  • Al-Mushayt O., Haq Kashiful, Yusuf Perwej, “Electronic-Government in Saudi Arabia; a Positive Revolution in the Peninsula”, International Transactions in Applied Sciences, India, ISSN-0974-7273, Volume 1, Number 1, Pages 87-98, July-December 2009
  • C. Y. Lin, T.-Y. Li and P. Chen, "An Information Visualization System to Assist News Topics Exploration with Social Media", ACMDL, July 2016
  • Mykhailo Granik and Volodymyr Mesyura, "Fake news detection using naive bayes classifier", 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), pp. 900-903, 2017
  • M. Luo, J.T. Hancock and D.M. Markowitz, "Credibility Perceptions and Detection Accuracy of Fake News Headlines on Social Media: Effects of Truth-Bias and Endorsement Cues", Comm. Research, vol. 49, no. 2, pp. 171-195, 2022
  • Niall J Conroy, Victoria L Rubin and Yimin Chen, "Automatic deception detection: Methods for finding fake news", Proceedings of the Association for Information Science and Technology, vol. 52, no. 1, pp. 1-4, 2015
  • Santhanam, Laura. 2017. “New poll: 70% of Americans think civility has gotten worse since Trump took office.” PBS News Hour, July 3. www.pbs.org/newshour/politics/new-poll-70-americans-think-civility-gotten-worse-since-trump-took-office
  • Allcott, H. and Gentzkow, M.,” Social media and fake news in the 2016 election”, Journal of Economic Perspectives, 31(2):211–36, 2017\
  • Shobhit Kumar Ravi, Shivam Chaturvedi, Dr. Neeta Rastogi, Dr. Nikhat Akhtar, Dr. Yusuf Perwej, “A Framework for Voting Behavior Prediction Using Spatial Data”, International Journal of Innovative Research in Computer Science & Technology (IJIRCST), ISSN: 2347-5552, Volume 10, Issue 2, Pages 19-28, 2022, DOI: 10.55524/ijircst.2022.10.2.4
  • William Yang Wang, "” liar liar pants on fire”: A new benchmark dataset for fake news detection", arXiv preprint, 2017
  • A. Al-Sideiri, Z. B. C. Cob, and S. B. M. Drus, Machine Learning Algorithms for Diabetes Prediction: A Review Paper,‖ ACM Int. Conf. Proceeding Ser., pp. 27–32, 2019, doi: 10.1145/3388218.3388231.
  • Yusuf Perwej, Dr. Ashish Chaturvedi, “Machine Recognition of Hand Written Characters using Neural Networks”, International Journal of Computer Applications (IJCA), USA, ISSN 0975 – 8887, Volume 14, No. 2, Pages 6- 9, 2011, DOI: 10.5120/1819-2380
  • Dr. E. Baraneetharan, Role of Machine Learning Algorithms Intrusion Detection in WSNs: A Survey, ‖ J. Inf. Technol. Digit. World, vol. 02, no. 03, pp. 161– 173, 2020, doi: 10.36548/jitdw.2020.3.004.
  • Y. Perwej, Firoj Parwej, Nikhat Akhtar, “An Intelligent Cardiac Ailment Prediction Using Efficient ROCK Algorithm and K- Means & C4.5 Algorithm”, European Journal of Engineering Research and Science (EJERS), Bruxelles, Belgium, ISSN: 2506-8016 (Online), Vol. 3, No. 12, Pages 126 – 134, 2018, DOI: 10.24018/ejers.2018.3.12.989
  • K. Thai, T. M. Tu, T. Q. Bui and T.-T. Bui, "Gradient tree boosting machine learning on predicting the failure modes of the RC panels under impact loads", Eng. Comput, pp. 1-12, 2019
  • A. Telikani, A. Tahmassebi, W. Banzhaf, and A. H. Gandomi, Evolutionary Machine Learning: A Survey, ACM Comput. Surv., vol. 54, no. 8, 2022
  • Wei Xiong, Bo Du, Lefei Zhang, Ruimin Hu and Dacheng Tao, "Regularizing Deep Convolutional Neural Networks with a Structured Decorrelation Constraint", IEEE 16th International Conference on Data Mining (ICDM), pp. 3366-3370, 2016
  • Y. Perwej, “An Evaluation of Deep Learning Miniature Concerning in Soft Computing”, International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), ISSN (Online): 2278-1021, ISSN (Print): 2319-5940, Volume 4, Issue 2, Pages 10 - 16, 2015, DOI: 10.17148/IJARCCE.2015.4203
  • N. Kwak, Introduction to Convolutional Neural Networks (CNNs), 2016
  • Z Li, C Ding, S Wang et al., "E-RNN: Design optimization for efficient recurrent neural networks in FPGAs[C]", 2019 IEEE International Symposium on High Performance Computer Architecture (HPCA), pp. 69-80, 2019
  • Y. Freund and R. Schapire, "A Decision-Theoretic Generalization of Online Learning and an Application to Boosting", Journal of Computer and System Sciences, vol. 55, pp. 119-139, 1997
  • Potthast, M.; Kiesel, J.; Reinartz, K.; Bevendorff, J.; Stein, B. A stylometric inquiry into hyperpartisan and fake news. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics; ACL: Melbourne, Australia, Volume 1, pp. 231–240, July 2018
  • Nikhat Akhtar, “Artificial Intelligence and Machine Learning in Human Resource Management for Sales research Perspective”, IEEE International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), Electronic ISBN:978-1-6654-7413-9, SCOPUS, ISBN:978-1-6654-7414-6, Chennai, India, 2022, DOI: 10.1109/ICSES55317.2022.9914086
  • Rubin, V.L., Conroy, N.J., Chen, Y., Cornwell, S.: Fake News or Truth? Using Satirical Cues to Detect Potentially Misleading News, pp. 7–17. https://youtu.be/2X93u3anTco. Accessed 29 July 2022
  • Neha Kulshrestha, Nikhat Akhtar, Dr. Yusuf Perwej, “Deep Learning Models for Object Recognition and Quality Surveillance”, International Conference on Emerging Trends in IoT and Computing Technologies (ICEICT-2022), ISBN 978-10324-852-49, Routledge, Taylor & Francis, CRC Press, Chapter 75, pages 508-518, Goel Institute of Technology & Management, Lucknow, 2022, DOI: 10.1201/9781003350057-75
  • J. C. Reis, A. Correia, F. Murai, A. Veloso and F. Benevenuto, "Supervised learning for fake news detection", IEEE Intelligent Systems, vol. 34, no. 2, pp. 76-81, 2019
  • S. Gilda, "Notice of violation of ieee publication principles: Evaluating machine learning algorithms for fake news detection", 2017 IEEE 15 th student conference on research and development (SCOReD). IEEE , pp. 110-115, 2017
  • Y. Perwej, “An Optimal Approach to Edge Detection Using Fuzzy Rule and Sobel Method”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (IJAREEIE), ISSN (Print) : 2320 – 3765, ISSN (Online): 2278 – 8875, Volume 4, Issue 11, Pages 9161-9179, 2015, DOI: 10.15662/IJAREEIE.2015.0411054
  • Y. Perwej, Dr. Ashish Chaturvedi, “Machine Recognition of Hand Written Characters using Neural Networks”, International Journal of Computer Applications (IJCA), USA, ISSN 0975 – 8887, Volume 14, No. 2, Pages 6- 9, 2011, DOI: 10.5120/1819-2380
  • Baptista JP, Gradim A,”Understanding fake news consumption: a review”, Soc Sci 9(10):185, 2020
  • K. Shu, S. Wang and H. Liu, "Understanding user profiles on social media for fake news detection", 2018 IEEE conference on multimedia information processing and retrieval (MIPR). IEEE, pp. 430-435, 2018
  • L. Cui, K. Shu, S. Wang, D. Lee and H. Liu, "defend: A system for explainable fake news detection", Proceedings of the 28th ACM international conference on information and knowledge management, pp. 2961-2964, 2019
  • Y. Perwej, F. Parwej, “A Neuroplasticity (Brain Plasticity) Approach to Use in Artificial Neural Network”, International Journal of Scientific & Engineering Research (IJSER), France , ISSN 2229 – 5518, Volume 3, Issue 6, Pages 1- 9, 2012, DOI: 10.13140/2.1.1693.2808
  • M. Weiler, M. Geiger, M. Welling, W. Boomsma and T. Cohen, "3D steerable CNNs: Learning rotationally equivariant features in volumetric data", arXiv:1807.02547, 2018, [online] Available: http://arxiv.org/abs/1807.02547
  • N. Akhtar, Devendera Agarwal, “An Efficient Mining for Recommendation System for Academics”, International Journal of Recent Technology and Engineering (IJRTE), ISSN 2277-3878 (online), SCOPUS, Volume-8, Issue-5, Pages 1619-1626, 2020, DOI: 10.35940/ijrte.E5924.018520
    • " target="_blank"> BibTeX     |
  • Prof. Kameswara Rao Poranki, Dr. Yusuf Perwej, Dr. Asif Perwej,” The Level of Customer Satisfaction related to GSM in India “, TIJ's Research Journal of Science & IT Management – RJSITM, International Journal's-Research Journal of Science & IT Management of Singapore, ISSN: 2251-1563, Singapore, in www.theinternationaljournal.org as RJSSM, Volume 04, Number: 03, Pages 29-36 , 2015
  • Guess A, Nagler J, Tucker J. Less than you think: Prevalence and predictors of fake news dissemination on Facebook. Science Advances. 2019;5: eaau4586. pmid:30662946
  • Nikhat Akhtar, Nazia Tabassum, Dr. Asif Perwej, Dr. Yusuf Perwej,“ Data Analytics and Visualization Using Tableau Utilitarian for COVID-19 (Coronavirus)”, Global Journal of Engineering and Technology Advances (GJETA), ISSN : 2582-5003, Volume 3, Issue 2, Pages 28-50, May 2020, DOI: 10.30574/gjeta.2020.3.2.0029
  • Kaliyar, R.K.; Goswami, A.; Narang, P. EchoFakeD: Improving fake news detection in social media with an efficient deep neural network. Neural Comput. Appl., 33, 8597–8613, 2021
  • Golbeck, J.; Mauriello, M.; Auxier, B.; Bhanushali, K.H.; Bonk, C.; Bouzaghrane, M.A.; Buntain, C.; Chanduka, R.; Cheakalos, P.; Everett, J.B.; et al. Fake News vs Satire: A Dataset and Analysis; WebSci ’18; Association for Computing Machinery: New York, NY, USA, pp. 17–21, 2018
  • Y. Perwej, Dr. Shaikh Abdul Hannan, Firoj Parwej, Nikhat Akhtar, “A Posteriori Perusal of Mobile Computing”, International Journal of Computer Applications Technology and Research (IJCATR), ATS (Association of Technology and Science), India, ISSN 2319–8656 (Online), Volume 3, Issue 9, Pages 569 - 578, September 2014, DOI: 10.7753/IJCATR0309.1008
  • Al-Mushayt O., Haq Kashiful, Yusuf Perwej, “Electronic-Government in Saudi Arabia; a Positive Revolution in the Peninsula”, International Transactions in Applied Sciences, India, ISSN-0974-7273, Volume 1, Number 1, Pages 87-98, July-December 2009
  • C. Y. Lin, T.-Y. Li and P. Chen, "An Information Visualization System to Assist News Topics Exploration with Social Media", ACMDL, July 2016
  • Mykhailo Granik and Volodymyr Mesyura, "Fake news detection using naive bayes classifier", 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), pp. 900-903, 2017
  • M. Luo, J.T. Hancock and D.M. Markowitz, "Credibility Perceptions and Detection Accuracy of Fake News Headlines on Social Media: Effects of Truth-Bias and Endorsement Cues", Comm. Research, vol. 49, no. 2, pp. 171-195, 2022
  • Niall J Conroy, Victoria L Rubin and Yimin Chen, "Automatic deception detection: Methods for finding fake news", Proceedings of the Association for Information Science and Technology, vol. 52, no. 1, pp. 1-4, 2015
  • Santhanam, Laura. 2017. “New poll: 70% of Americans think civility has gotten worse since Trump took office.” PBS News Hour, July 3. www.pbs.org/newshour/politics/new-poll-70-americans-think-civility-gotten-worse-since-trump-took-office
  • Allcott, H. and Gentzkow, M.,” Social media and fake news in the 2016 election”, Journal of Economic Perspectives, 31(2):211–36, 2017\
  • Shobhit Kumar Ravi, Shivam Chaturvedi, Dr. Neeta Rastogi, Dr. Nikhat Akhtar, Dr. Yusuf Perwej, “A Framework for Voting Behavior Prediction Using Spatial Data”, International Journal of Innovative Research in Computer Science & Technology (IJIRCST), ISSN: 2347-5552, Volume 10, Issue 2, Pages 19-28, 2022, DOI: 10.55524/ijircst.2022.10.2.4
  • William Yang Wang, "” liar liar pants on fire”: A new benchmark dataset for fake news detection", arXiv preprint, 2017
  • A. Al-Sideiri, Z. B. C. Cob, and S. B. M. Drus, Machine Learning Algorithms for Diabetes Prediction: A Review Paper,‖ ACM Int. Conf. Proceeding Ser., pp. 27–32, 2019, doi: 10.1145/3388218.3388231.
  • Yusuf Perwej, Dr. Ashish Chaturvedi, “Machine Recognition of Hand Written Characters using Neural Networks”, International Journal of Computer Applications (IJCA), USA, ISSN 0975 – 8887, Volume 14, No. 2, Pages 6- 9, 2011, DOI: 10.5120/1819-2380
  • Dr. E. Baraneetharan, Role of Machine Learning Algorithms Intrusion Detection in WSNs: A Survey, ‖ J. Inf. Technol. Digit. World, vol. 02, no. 03, pp. 161– 173, 2020, doi: 10.36548/jitdw.2020.3.004.
  • Y. Perwej, Firoj Parwej, Nikhat Akhtar, “An Intelligent Cardiac Ailment Prediction Using Efficient ROCK Algorithm and K- Means & C4.5 Algorithm”, European Journal of Engineering Research and Science (EJERS), Bruxelles, Belgium, ISSN: 2506-8016 (Online), Vol. 3, No. 12, Pages 126 – 134, 2018, DOI: 10.24018/ejers.2018.3.12.989
  • K. Thai, T. M. Tu, T. Q. Bui and T.-T. Bui, "Gradient tree boosting machine learning on predicting the failure modes of the RC panels under impact loads", Eng. Comput, pp. 1-12, 2019
  • A. Telikani, A. Tahmassebi, W. Banzhaf, and A. H. Gandomi, Evolutionary Machine Learning: A Survey, ACM Comput. Surv., vol. 54, no. 8, 2022
  • Wei Xiong, Bo Du, Lefei Zhang, Ruimin Hu and Dacheng Tao, "Regularizing Deep Convolutional Neural Networks with a Structured Decorrelation Constraint", IEEE 16th International Conference on Data Mining (ICDM), pp. 3366-3370, 2016
  • Y. Perwej, “An Evaluation of Deep Learning Miniature Concerning in Soft Computing”, International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), ISSN (Online): 2278-1021, ISSN (Print): 2319-5940, Volume 4, Issue 2, Pages 10 - 16, 2015, DOI: 10.17148/IJARCCE.2015.4203
  • N. Kwak, Introduction to Convolutional Neural Networks (CNNs), 2016
  • Z Li, C Ding, S Wang et al., "E-RNN: Design optimization for efficient recurrent neural networks in FPGAs[C]", 2019 IEEE International Symposium on High Performance Computer Architecture (HPCA), pp. 69-80, 2019
  • Y. Freund and R. Schapire, "A Decision-Theoretic Generalization of Online Learning and an Application to Boosting", Journal of Computer and System Sciences, vol. 55, pp. 119-139, 1997
  • Potthast, M.; Kiesel, J.; Reinartz, K.; Bevendorff, J.; Stein, B. A stylometric inquiry into hyperpartisan and fake news. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics; ACL: Melbourne, Australia, Volume 1, pp. 231–240, July 2018
  • Nikhat Akhtar, “Artificial Intelligence and Machine Learning in Human Resource Management for Sales research Perspective”, IEEE International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), Electronic ISBN:978-1-6654-7413-9, SCOPUS, ISBN:978-1-6654-7414-6, Chennai, India, 2022, DOI: 10.1109/ICSES55317.2022.9914086
  • Rubin, V.L., Conroy, N.J., Chen, Y., Cornwell, S.: Fake News or Truth? Using Satirical Cues to Detect Potentially Misleading News, pp. 7–17. https://youtu.be/2X93u3anTco. Accessed 29 July 2022
  • Neha Kulshrestha, Nikhat Akhtar, Dr. Yusuf Perwej, “Deep Learning Models for Object Recognition and Quality Surveillance”, International Conference on Emerging Trends in IoT and Computing Technologies (ICEICT-2022), ISBN 978-10324-852-49, Routledge, Taylor & Francis, CRC Press, Chapter 75, pages 508-518, Goel Institute of Technology & Management, Lucknow, 2022, DOI: 10.1201/9781003350057-75
  • J. C. Reis, A. Correia, F. Murai, A. Veloso and F. Benevenuto, "Supervised learning for fake news detection", IEEE Intelligent Systems, vol. 34, no. 2, pp. 76-81, 2019
  • S. Gilda, "Notice of violation of ieee publication principles: Evaluating machine learning algorithms for fake news detection", 2017 IEEE 15 th student conference on research and development (SCOReD). IEEE , pp. 110-115, 2017
  • Y. Perwej, “An Optimal Approach to Edge Detection Using Fuzzy Rule and Sobel Method”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (IJAREEIE), ISSN (Print) : 2320 – 3765, ISSN (Online): 2278 – 8875, Volume 4, Issue 11, Pages 9161-9179, 2015, DOI: 10.15662/IJAREEIE.2015.0411054
  • Y. Perwej, Dr. Ashish Chaturvedi, “Machine Recognition of Hand Written Characters using Neural Networks”, International Journal of Computer Applications (IJCA), USA, ISSN 0975 – 8887, Volume 14, No. 2, Pages 6- 9, 2011, DOI: 10.5120/1819-2380
  • Baptista JP, Gradim A,”Understanding fake news consumption: a review”, Soc Sci 9(10):185, 2020
  • K. Shu, S. Wang and H. Liu, "Understanding user profiles on social media for fake news detection", 2018 IEEE conference on multimedia information processing and retrieval (MIPR). IEEE, pp. 430-435, 2018
  • L. Cui, K. Shu, S. Wang, D. Lee and H. Liu, "defend: A system for explainable fake news detection", Proceedings of the 28th ACM international conference on information and knowledge management, pp. 2961-2964, 2019
  • Y. Perwej, F. Parwej, “A Neuroplasticity (Brain Plasticity) Approach to Use in Artificial Neural Network”, International Journal of Scientific & Engineering Research (IJSER), France , ISSN 2229 – 5518, Volume 3, Issue 6, Pages 1- 9, 2012, DOI: 10.13140/2.1.1693.2808
  • M. Weiler, M. Geiger, M. Welling, W. Boomsma and T. Cohen, "3D steerable CNNs: Learning rotationally equivariant features in volumetric data", arXiv:1807.02547, 2018, [online] Available: http://arxiv.org/abs/1807.02547
  • N. Akhtar, Devendera Agarwal, “An Efficient Mining for Recommendation System for Academics”, International Journal of Recent Technology and Engineering (IJRTE), ISSN 2277-3878 (online), SCOPUS, Volume-8, Issue-5, Pages 1619-1626, 2020, DOI: 10.35940/ijrte.E5924.018520
    • " target="_blank">RIS     |
  • Prof. Kameswara Rao Poranki, Dr. Yusuf Perwej, Dr. Asif Perwej,” The Level of Customer Satisfaction related to GSM in India “, TIJ's Research Journal of Science & IT Management – RJSITM, International Journal's-Research Journal of Science & IT Management of Singapore, ISSN: 2251-1563, Singapore, in www.theinternationaljournal.org as RJSSM, Volume 04, Number: 03, Pages 29-36 , 2015
  • Guess A, Nagler J, Tucker J. Less than you think: Prevalence and predictors of fake news dissemination on Facebook. Science Advances. 2019;5: eaau4586. pmid:30662946
  • Nikhat Akhtar, Nazia Tabassum, Dr. Asif Perwej, Dr. Yusuf Perwej,“ Data Analytics and Visualization Using Tableau Utilitarian for COVID-19 (Coronavirus)”, Global Journal of Engineering and Technology Advances (GJETA), ISSN : 2582-5003, Volume 3, Issue 2, Pages 28-50, May 2020, DOI: 10.30574/gjeta.2020.3.2.0029
  • Kaliyar, R.K.; Goswami, A.; Narang, P. EchoFakeD: Improving fake news detection in social media with an efficient deep neural network. Neural Comput. Appl., 33, 8597–8613, 2021
  • Golbeck, J.; Mauriello, M.; Auxier, B.; Bhanushali, K.H.; Bonk, C.; Bouzaghrane, M.A.; Buntain, C.; Chanduka, R.; Cheakalos, P.; Everett, J.B.; et al. Fake News vs Satire: A Dataset and Analysis; WebSci ’18; Association for Computing Machinery: New York, NY, USA, pp. 17–21, 2018
  • Y. Perwej, Dr. Shaikh Abdul Hannan, Firoj Parwej, Nikhat Akhtar, “A Posteriori Perusal of Mobile Computing”, International Journal of Computer Applications Technology and Research (IJCATR), ATS (Association of Technology and Science), India, ISSN 2319–8656 (Online), Volume 3, Issue 9, Pages 569 - 578, September 2014, DOI: 10.7753/IJCATR0309.1008
  • Al-Mushayt O., Haq Kashiful, Yusuf Perwej, “Electronic-Government in Saudi Arabia; a Positive Revolution in the Peninsula”, International Transactions in Applied Sciences, India, ISSN-0974-7273, Volume 1, Number 1, Pages 87-98, July-December 2009
  • C. Y. Lin, T.-Y. Li and P. Chen, "An Information Visualization System to Assist News Topics Exploration with Social Media", ACMDL, July 2016
  • Mykhailo Granik and Volodymyr Mesyura, "Fake news detection using naive bayes classifier", 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), pp. 900-903, 2017
  • M. Luo, J.T. Hancock and D.M. Markowitz, "Credibility Perceptions and Detection Accuracy of Fake News Headlines on Social Media: Effects of Truth-Bias and Endorsement Cues", Comm. Research, vol. 49, no. 2, pp. 171-195, 2022
  • Niall J Conroy, Victoria L Rubin and Yimin Chen, "Automatic deception detection: Methods for finding fake news", Proceedings of the Association for Information Science and Technology, vol. 52, no. 1, pp. 1-4, 2015
  • Santhanam, Laura. 2017. “New poll: 70% of Americans think civility has gotten worse since Trump took office.” PBS News Hour, July 3. www.pbs.org/newshour/politics/new-poll-70-americans-think-civility-gotten-worse-since-trump-took-office
  • Allcott, H. and Gentzkow, M.,” Social media and fake news in the 2016 election”, Journal of Economic Perspectives, 31(2):211–36, 2017\
  • Shobhit Kumar Ravi, Shivam Chaturvedi, Dr. Neeta Rastogi, Dr. Nikhat Akhtar, Dr. Yusuf Perwej, “A Framework for Voting Behavior Prediction Using Spatial Data”, International Journal of Innovative Research in Computer Science & Technology (IJIRCST), ISSN: 2347-5552, Volume 10, Issue 2, Pages 19-28, 2022, DOI: 10.55524/ijircst.2022.10.2.4
  • William Yang Wang, "” liar liar pants on fire”: A new benchmark dataset for fake news detection", arXiv preprint, 2017
  • A. Al-Sideiri, Z. B. C. Cob, and S. B. M. Drus, Machine Learning Algorithms for Diabetes Prediction: A Review Paper,‖ ACM Int. Conf. Proceeding Ser., pp. 27–32, 2019, doi: 10.1145/3388218.3388231.
  • Yusuf Perwej, Dr. Ashish Chaturvedi, “Machine Recognition of Hand Written Characters using Neural Networks”, International Journal of Computer Applications (IJCA), USA, ISSN 0975 – 8887, Volume 14, No. 2, Pages 6- 9, 2011, DOI: 10.5120/1819-2380
  • Dr. E. Baraneetharan, Role of Machine Learning Algorithms Intrusion Detection in WSNs: A Survey, ‖ J. Inf. Technol. Digit. World, vol. 02, no. 03, pp. 161– 173, 2020, doi: 10.36548/jitdw.2020.3.004.
  • Y. Perwej, Firoj Parwej, Nikhat Akhtar, “An Intelligent Cardiac Ailment Prediction Using Efficient ROCK Algorithm and K- Means & C4.5 Algorithm”, European Journal of Engineering Research and Science (EJERS), Bruxelles, Belgium, ISSN: 2506-8016 (Online), Vol. 3, No. 12, Pages 126 – 134, 2018, DOI: 10.24018/ejers.2018.3.12.989
  • K. Thai, T. M. Tu, T. Q. Bui and T.-T. Bui, "Gradient tree boosting machine learning on predicting the failure modes of the RC panels under impact loads", Eng. Comput, pp. 1-12, 2019
  • A. Telikani, A. Tahmassebi, W. Banzhaf, and A. H. Gandomi, Evolutionary Machine Learning: A Survey, ACM Comput. Surv., vol. 54, no. 8, 2022
  • Wei Xiong, Bo Du, Lefei Zhang, Ruimin Hu and Dacheng Tao, "Regularizing Deep Convolutional Neural Networks with a Structured Decorrelation Constraint", IEEE 16th International Conference on Data Mining (ICDM), pp. 3366-3370, 2016
  • Y. Perwej, “An Evaluation of Deep Learning Miniature Concerning in Soft Computing”, International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), ISSN (Online): 2278-1021, ISSN (Print): 2319-5940, Volume 4, Issue 2, Pages 10 - 16, 2015, DOI: 10.17148/IJARCCE.2015.4203
  • N. Kwak, Introduction to Convolutional Neural Networks (CNNs), 2016
  • Z Li, C Ding, S Wang et al., "E-RNN: Design optimization for efficient recurrent neural networks in FPGAs[C]", 2019 IEEE International Symposium on High Performance Computer Architecture (HPCA), pp. 69-80, 2019
  • Y. Freund and R. Schapire, "A Decision-Theoretic Generalization of Online Learning and an Application to Boosting", Journal of Computer and System Sciences, vol. 55, pp. 119-139, 1997
  • Potthast, M.; Kiesel, J.; Reinartz, K.; Bevendorff, J.; Stein, B. A stylometric inquiry into hyperpartisan and fake news. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics; ACL: Melbourne, Australia, Volume 1, pp. 231–240, July 2018
  • Nikhat Akhtar, “Artificial Intelligence and Machine Learning in Human Resource Management for Sales research Perspective”, IEEE International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), Electronic ISBN:978-1-6654-7413-9, SCOPUS, ISBN:978-1-6654-7414-6, Chennai, India, 2022, DOI: 10.1109/ICSES55317.2022.9914086
  • Rubin, V.L., Conroy, N.J., Chen, Y., Cornwell, S.: Fake News or Truth? Using Satirical Cues to Detect Potentially Misleading News, pp. 7–17. https://youtu.be/2X93u3anTco. Accessed 29 July 2022
  • Neha Kulshrestha, Nikhat Akhtar, Dr. Yusuf Perwej, “Deep Learning Models for Object Recognition and Quality Surveillance”, International Conference on Emerging Trends in IoT and Computing Technologies (ICEICT-2022), ISBN 978-10324-852-49, Routledge, Taylor & Francis, CRC Press, Chapter 75, pages 508-518, Goel Institute of Technology & Management, Lucknow, 2022, DOI: 10.1201/9781003350057-75
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