S
Sirshendu Hore
Researcher at Hooghly Engineering and Technology College
Publications - 27
Citations - 810
Sirshendu Hore is an academic researcher from Hooghly Engineering and Technology College. The author has contributed to research in topics: Authentication & Artificial neural network. The author has an hindex of 13, co-authored 26 publications receiving 696 citations.
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Journal ArticleDOI
Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings
Sankhadeep Chatterjee,Sarbartha Sarkar,Sirshendu Hore,Nilanjan Dey,Amira S. Ashour,Valentina Emilia Balas +5 more
TL;DR: A particle swarm optimization-based approach to train the NN (NN-PSO), capable to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistory reinforced concrete building structure in the future.
Journal ArticleDOI
An Integrated Interactive Technique for Image Segmentation using Stack based Seeded Region Growing and Thresholding
Sirshendu Hore,Souvik Chakraborty,Sankhadeep Chatterjee,Nilanjan Dey,Amira S. Ashour,Le Van Chung,Dac-Nhuong Le +6 more
TL;DR: A novel real time integrated method to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation established that the proposed integrated method outperformed the region growing method in terms of the recall and F-score.
Book ChapterDOI
Indian Sign Language Recognition Using Optimized Neural Networks
Sirshendu Hore,Sankhadeep Chatterjee,V. Santhi,Nilanjan Dey,Amira S. Ashour,Valentina Emilia Balas,Fuqian Shi +6 more
TL;DR: In this paper, three novel methods were reported to solve the problem of recognition of Indian sign language gestures effectively by combining Neural Network (NN) with Genetic Algorithm (GA), Evolutionary algorithm (EA) and Particle Swarm Optimization (PSO) separately to attain novel NN-GA, NN -EA and NNPSO methods; respectively.
Book ChapterDOI
Dengue Fever Classification Using Gene Expression Data: A PSO Based Artificial Neural Network Approach
TL;DR: A novel application of Particle Swarm Optimization (PSO) trained Artificial Neural Network (ANN) has been employed to separate the patients having Dengue fevers from those who are recovering from it or do not have DF.
Book ChapterDOI
Forest Type Classification: A Hybrid NN-GA Model Based Approach
TL;DR: The authors have proposed a GA trained Neural Network classifier to tackle the task of classify tree species and one mixed forest class using geographically weighted variables calculated for Cryptomeria japonica and Chamaecyparis obtusa.