Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Particle swarm optimization

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    Spectral-spatial MODIS image analysis using swarm intelligence algorithms and region based segmentation for flood assessment
    (Springer Verlag service@springer.de, 2013) Senthilnath, J.; Vikram Shenoy, H.; Omkar, S.N.; Mani, V.
    This paper discusses an approach for river mapping and flood evaluation based on multi-temporal time-series analysis of satellite images utilizing pixel spectral information for image clustering and region based segmentation for extracting water covered regions. MODIS satellite images are analyzed at two stages: before flood and during flood. Multi-temporal MODIS images are processed in two steps. In the first step, clustering algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to distinguish the water regions from the non-water based on spectral information. These algorithms are chosen since they are quite efficient in solving multi-modal optimization problems. These classified images are then segmented using spatial features of the water region to extract the river. From the results obtained, we evaluate the performance of the methods and conclude that incorporating region based image segmentation along with clustering algorithms provides accurate and reliable approach for the extraction of water covered region. © 2013 Springer.
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    Clustering using levy flight cuckoo search
    (Springer Verlag service@springer.de, 2013) Senthilnath, J.; Das, V.; Omkar, S.N.; Mani, V.
    In this paper, a comparative study is carried using three nature-inspired algorithms namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Cuckoo Search (CS) on clustering problem. Cuckoo search is used with levy flight. The heavy-tail property of levy flight is exploited here. These algorithms are used on three standard benchmark datasets and one real-time multi-spectral satellite dataset. The results are tabulated and analysed using various techniques. Finally we conclude that under the given set of parameters, cuckoo search works efficiently for majority of the dataset and levy flight plays an important role. © 2013 Springer.
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    Optimization of soil parameters in multiple layers of ground structure
    (IEEE Computer Society, 2017) Senthilkumar, R.T.
    This paper proposes an optimization methodology to estimate the parameters of multilayer earth structure by using the hybrid genetic algorithm and particle swarm optimization. By using four wire Wenner method on the ground is to acquire the experimental apparent resistivity curve. With the measured experimental apparent resistivity, can compute the theoretical apparent resistivity curve and estimate the soil parameters such as a number of layers, thickness of each layer (Nth layer thickness is infinity) and its resistivity. The representation of unknown soil is determined by comparing the closeness of experimental apparent resistivity curve with the theoretical optimized apparent resistivity. © 2017 IEEE.
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    Optimization of adaptive resonance theory neural network using particle swarm optimization technique
    (Springer Verlag service@springer.de, 2018) Satpute, K.; Kumar, R.
    With the advancement of computers and its computational enhancement over several decades of use, but with the growth in the dependencies and use of these systems, more and more concerns over the risk and security issues in networks have raised. In this paper, we have proposed approach using particle swarm optimization to optimize ART. Adaptive resonance theory is one of the most well-known machine-learning-based unsupervised neural networks, which can efficiently handle high-dimensional dataset. PSO on the other hand is a swarm intelligence-based algorithm, efficient in nonlinear optimization problem and easy to implement. The method is based on anomaly detection as it can also detect unknown attack types. PSO is used to optimize vigilance parameter of ART-1 and to classify network data into attack or normal. KDD ’99 (knowledge discovery and data mining) dataset has been used for this purpose. © Springer Nature Singapore Pte Ltd. 2018.
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    Comparative Analysis on Diverse Heuristic-Based Joint Probabilistic Data Association for Multi-target Tracking in a Cluttered Environment
    (Springer Science and Business Media Deutschland GmbH, 2022) Lingadevaru, P.; Srihari, P.
    The target tracking using the passive multi-static radar system produces various detections via distinct signal propagation paths. Trackers solve the uncertainties that arise from the measurement path as well as the measurement origin. The existing multi-target tracking algorithms suffer from high computational loads, because they require the entire probable joint measurement-to-track assignments. This paper proposes to develop a comparative analysis on diverse heuristic algorithms for implementing the optimized JDPA model for tracking multiple targets using multi-static passive radar system in the presence of clutter. Here, the nature-inspired algorithms like Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) are used for analyzing the optimized JDPA model for tracking multiple targets in multi-static passive scenario. This paper further, aims to tune the position and velocity of the tracker towards the target using two heuristic algorithms, and intends to analyze the effect of those algorithms on improving the performance of multiple target tracking. The key objective of the proposed model is to minimize the Mean Absolute Error (MAE) between the estimated trajectory of the track and the true target state. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.