A5086072976- Remote-Sensing Image Classification
- Metaheuristic Optimization Algorithms Research
- Advanced Clustering Algorithms Research
- Remote Sensing and Land Use
- Survey Sampling and Estimation Techniques
- Retinal Imaging and Analysis
- Quantum Computing Algorithms and Architecture
- Face and Expression Recognition
- Machine Learning and Algorithms
- Image Retrieval and Classification Techniques
- Advanced Image Fusion Techniques
- Fractal and DNA sequence analysis
- Infrared Target Detection Methodologies
- Optimization and Search Problems
- Image and Signal Denoising Methods
Presidency University
2021-2023
Banaras Hindu University
2021
Assam University
2021
University of Burdwan
2020
In recent times, several metaheuristic algorithms have been proposed for solving real world optimization problems. this paper, a new algorithm, called the Border Collie Optimization is introduced. The algorithm developed by mimicking sheep herding styles of dogs. Collie's unique style from front as well sides adopted successfully in paper. entire population divided into two parts viz., dogs and sheep. This done to equally focus on both exploration exploitation search space. utilizes...
Hyperspectral images contain a wide variety of information, varying from relatively large regions to smaller manmade buildings, roads and others. Automatic clustering various in such is tedious task. A multilevel quantum inspired fractional order ant colony optimization algorithm proposed this paper for automatic hyperspectral images. Application pheromone updation technique the produces more accurate results. Moreover, version results faster than its classical counterpart. new band fusion...
Hyperspectral images contain one-dimensional spectral data and two-dimensional spatial data. A novel automatic clustering algorithm using qutrit exponential decomposition based particle swarm optimization is proposed for automatically determining the number of optimized cluster segments in hyper-spectral images. The compared with classical decay algorithms establishing its efficiency. quality segmentation determined F score.
In the present paper, we have tried to discover a new calibration estimator of population mean under stratified two-phase sampling design utilizing information on two auxiliary variables. order develop estimator, obtained optimum stratum weights so that MSE/Variance would be minimized. Moreover, compared efficiency proposed with considered scheme using single variable. An empirical illustration along simulation analysis has also been presented demonstrate theoretical results.