Jyoti A. Kendule

Work place: SKN SCOE, Korti, Pandharpur, India

E-mail: jakendule@coe.sveri.ac.in

Website: https://orcid.org/0000-0000-0003-3501-1562

Research Interests: Image Processing, Analysis of Algorithms


Ms. Jyoti A. Kendule, received her M. E. degree in Electronics and Telecommunication Engineering from Solapur University, Solapur, India in 2014 and Pursuing Ph. D degree from PAH Solapur University, Solapur, India. She is working as Assistant Professor, in the department of Electronics and Telecommunication, SVERIs COE, Pandharpur, Solapur, India. Her Research areas of interest include Image Processing, Internet of Things. She published more than 20 papers in various conferences and journals. Under her guidance ten M.E.s has been awarded.

Author Articles
Variable Synergic Squeeze Convoluted Equilibrium Network Enabled Crowd Management in IoT

By Jyoti A. Kendule Kailash J. Karande

DOI: https://doi.org/10.5815/ijigsp.2023.04.06, Pub. Date: 8 Aug. 2023

In IoT, Crowd counting is a difficult task, because of any sudden incidents people unites in a particular place. To count them effectively a crowd counting mechanism is needed. The crowd counting is help for public security. Several methods are proposed for crowd counting, but the existing methods does not provide high accuracy and high error rate. To overcome these drawbacks a Variable Synergic Squeeze Convoluted Equilibrium Network Enabled Crowd Management in IoT (VS2CEN-CC-IOT) is proposed in this manuscript for crowd counting and crowd density detection. Initially, the images are taken from two datasets named ShanghaiTech and Venice dataset. Then the images are preprocessed using Gaussian filter based preprocessing. After preprocessing the discrete wavelet transform (DWT) is used for extracting the features. The extracted features are then given to Synergic Squeeze Convoluted Equilibrium Network (SSCEN) for detecting crowd count and crowd density. In this work, variable Equilibrium Optimization Algorithm (EOA) is applied to optimize the weight parameter of SSCEN. The simulation procedure is performed in PYTHON platform. The VS^2CEN-CC-IOT attains 0.8%, 1.3%, 1.5% higher accuracy, 13%, 3.3%, 8.2% higher Precision, 12%, 10%, 17% higher specificity , 8.2%, 3.3%, 6.9% higher F1-score and 0.12%, 0.06%, 0.07% lower mean absolute error (MAE), 0.2%, 0.25%, 0.1% lower root mean square error than the existing optimization approaches such as Arithmetic Optimization Algorithm(ADA), Chaos Game Optimization(CGO) and Gradient Based Optimizer(GBO) respectively.

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