1 Affiliation : Department of CSE, Chandigarh University, Punjab, India
Email : firstname.lastname@example.org
2 Affiliation : Department of CSE, Chandigarh University, Punjab, India
Email : email@example.com
With the expanding populace, evaluating swarm thickness is a typical issue for swarm observation in Computer Vision. This issue stays a difficult assignment because of various varieties in scale issues created by various blocked uproars, changing shapes, and point of view variety. To handles these difficulties and to give a decent condition of precision we, in this way, center to gather a tremendous measure of datasets with shifting thickness levels and manufacture an Allied-CNN model. The assortment of the datasets is done from different sources like YouTube and some genuine recordings. The Allied-CNN model is worked in python and prepared on a named dataset of thousand item pictures from different points of view, for deciding thickness levels (as low thickness, medium thickness, and high thickness). Preparing results for thickness estimation show the preparation set precision arrives at 94.8%, the greatest approval exactness of just 88% is accomplished. Along these lines, this model aids in ordering a picture as low thickness, medium thickness, and high thickness. Estimations on this group datasets show that the proposed Allied-CNN performs modest outcomes contrasted with the cutting-edge strategies.
Community , Modelling , Neural Network , Machine Learning , convolution neural network , perceptron
 W. M. Shalash, A. A. AlZahrani and S. H. Al-Nufaii, "Crowd Detection Management System," 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS), Riyadh, Saudi Arabia, 2019, pp. 1-8, doi: 10.1109/CAIS.2019.8769566.
 “Crowd Disaster in India” [Online] Available: https://blog.forumias.com/crowd-disasters-and-management-in-india/
 V. A. Sindagi and V. M. Patel, "CNN-Based cascaded multi-task learning of high-level prior and density estimation for crowd counting," 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Lecce, 2017, pp. 1-6, doi: 10.1109/AVSS.2017.8078491.
 M. V. Anees and S. G. Kumar, "Deep Learning Framework for Density Estimation of Crowd Videos," 2018 8th International Symposium on Embedded Computing and System Design (ISED), Cochin, India, 2018, pp. 16-20.
 J. Qiu, W. Wan, H. Yao and K. Han, "Crowd counting and density estimation via a two-column convolutional neural network," 4th International Conference on Smart and Sustainable City (ICSSC 2017), Shanghai, 2017, pp. 1-5.
 Shiliang Pu, Tao Song, Yuan Zhang, and Di Xie, “Estimation of Crowd Density In Surveillance Scenes Based On Deep Convolutional Neural Network”. 8th International Conference on Advances in Information Technology, IAIT 2016, 19-22 December 2016, Macau, China.
 Xiaohang Xu, Dongming Zhang, and Hong Zheng, “Crowd Density Estimation Of Scenic Spots Based On Multi-Feature Ensemble Learning”, Journal Of Electrical And Computer Engineering, Vol. 2017, Article Id 2580860, 12 Pages, 2017.
 D. Kim, Y. Lee, B. Ku and H. Ko, "Crowd Density Estimation Using Multi-class Adaboost," 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance, Beijing, 2012, pp. 447-451.
 A.K. Pai, A. K. Karunakar and U. Raghavendra, "A Novel Crowd Density Estimation Technique using Local Binary Pattern and Gabor Features," 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Lecce, 2017, pp. 1-6.
 V. Huynh, V. Tran and C. Huang, "IUML: Inception U-Net Based Multi-Task Learning For Density Level Classification And Crowd Density Estimation," 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), Bari, Italy, 2019, pp. 3019-3024.
 N. Ilyas, A. Ahmad, and K. Kim, "CASA-Crowd: A Context-Aware Scale Aggregation CNN-Based Crowd Counting Technique," in IEEE Access, vol. 7, pp. 182050-182059, 2019.
 Zeng, Xin & Wu, Yunpeng & Hu, Shizhe & Wang, Ruobin & Ye, Yangdong. (2019). DSPNet: Deep Scale Purifier Network for Dense Crowd Counting. Expert Systems with Applications. 141. 112977. 10.1016/j.eswa.2019.112977.
 Y. Tian, Y. Lei, J. Zhang, and J. Z. Wang, "PaDNet: Pan-Density Crowd Counting," in IEEE Transactions on Image Processing, vol. 29, pp. 2714-2727, 2020.
 A. L. Hettiarachchi, H. O. Thathsarani, P. U. Wickramasinghe, D. S. Wickramasuriya and R. Rodrigo, "Extensible video surveillance software with simultaneous event detection for low and high density crowd analysis," 7th International Conference on Information and Automation for Sustainability, Colombo, 2014, pp. 1-6.
 B. Li, X. Han and D. Wu, "Real-Time Crowd Density Estimation Based on Convolutional Neural Networks," 2018 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), Xiamen, 2018, pp. 690-694.
 P. Badatia and P. P. Tasgaonkar, "Crowd counting and Density Estimation using Multicolumn Discriminator in GAN," 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Bangalore, 2018, pp. 1179-1183.
 Pavel Golik, Patrick Doetsch, and Hermann Ney. “Cross-entropy vs. squared error training: a theoretical and experimental comparison”. In Interspeech, volume 13, pages 1756–1760, 2013.
 “Crowded People “[Online] Available: https://www.videezy.com/free-video/
 M. Fu, P. Xu, X. Li, Q. Liu, M. Ye, and C. Zhu, “Fast crowd density estimation with convolutional neural networks,” Engineering Applications of Artificial Intelligence, vol. 43, pp. 81–88, 2015.
 P. V. V. Kishore, R. Rahul, K. Sravya, and A. S. C. S. Sastry, “Crowd Density Analysis and tracking,” in Proceedings of the International Conference on Advances in Computing, Communications and Informatics (ICACCI '15), pp. 1209–1213, India, August 2015.
 M. Kumar, P. Mukherjee, K. Verma, S. Verma and D. B. Rawat, "Improved Deep Convolutional Neural Network based Malicious Node Detection and Energy-Efficient Data Transmission in Wireless Sensor Networks," in IEEE Transactions on Network Science and Engineering, doi: 10.1109/TNSE.2021.3098011.
 Tanvi Sharma, et al.. (2017) Intelligent Heart Disease Prediction System Using Machine Learning: A Review, International Journal of Recent Research Aspects, ISSN: 2349-7688, Vol. 4, Issue 2, pp. 94- 97.
 Akshat Srivastava et al, Analysis of Quality of Service in VANET, 2020 IOP Conf. Ser.: Mater. Sci. Eng. 993 012061
 Loveleen Gaur, Gurmeet Singh, Arun Solanki, Noor Zaman Jhanjhi, Ujwal Bhatia, Shavneet Sharma, Sahil Verma, Kavita, Nataša Petrović, Muhammad Fazal Ijaz, and Wonjoon Kim, Disposition of Youth in Predicting Sustainable Development Goals Using the Neuro-fuzzy and Random Forest Algorithms, Article number: 11:24 (2021)
 Monica Sood, et.al.“Optimal Path Planning using Swarm Intelligence based Hybrid Techniques” Journal of computational and theoretical nanoscience (JCTN), ASPBS publisher. Vol. 16 No. 9, 2019, pp. 3717–3727, DOI:10.1166/jctn.2019.8240.
 Kaur Manjit; et al. “Flying Ad-Hoc Network (FANET): Challenges and Routing Protocols” Journal of Computational and Theoretical Nanoscience, Volume 17, Number 6, June 2020, pp. 2575-2581(7), https://doi.org/10.1166/jctn.2020.8932
 Ghosh, Gopal; et al. ‘Internet of Things based video surveillance systems for security applications’ Journal of Computational and Theoretical Nanoscience, Volume 17, Number 6, June 2020, pp. 2582-2588(7) https://doi.org/10.1166/jctn.2020.8933
 Gopal Ghosh, et al. ‘A Systematic Review on Image Encryption Techniques’ Turkish Journal of Computer and Mathematics Education, Vol.12 No.10 (2021), 3055-3059 M. Balazinska et al., “Data management in the worldwide sensor web,” IEEE Pervasive Comput., vol. 6, no. 2, pp. 30–40, 2007, doi: 10.1109/MPRV.2007.27.
 A. Hussain et al., "A Resource Efficient hybrid Proxy Mobile IPv6 extension for Next Generation IoT Networks," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2021.3058982.
 Gandam A, Sidhu JS, Verma S, Jhanjhi NZ, Nayyar A, Abouhawwash M, et al. (2021) An efficient post-processing adaptive filtering technique to rectifying the flickering effects. PLoS ONE 16(5): e0250959. https://doi.org/10.1371/journal.pone.0250959