Kailash J. Karande

Work place: SKN, SCOE, Korti, Pandharpur, India

E-mail: kailashkarande@yahoo.co.in

Website: https://orcid.org/0000-0000-0002-3089-8937

Research Interests: Pattern Recognition


Dr. K. J. Karande is the Director and Principal of SKN Sinhagad College of Eng., Pandharpur, India. His research interest encompasses a wide range of activities with a focus on face recognition and developed a comparative approach to ICA analysis. A strong electronics background combined with FRT has enabled him and his team to produce PCA-ICA modules for the specific application. He has published 10 books and 80 + research papers (h- index 10, i-10 index 10) in various national and international journals and 2 patents from the Indian Government. He is working as a PI on a research project funded by Punyashlok Ahilyadevi Holkar Solapur University Solapur, India. He received the European Fellowship of Erasmus Mundus program at Albert Nelson University, Denmark. Also, he was awarded as best principal from Punyashlok Ahilyadevi Holkar Solapur university Solapur and many more for his contribution to the field of education. He is acting as editor for various journal books of Lambert Publication, Springers, CRC Press TandF Groups, IOP publishing, and AIP publishing.

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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