scholarly journals Remote Sensing Imagery Segmentation: A Hybrid Approach

2021 ◽  
Vol 13 (22) ◽  
pp. 4604
Author(s):  
Shreya Pare ◽  
Himanshu Mittal ◽  
Mohammad Sajid ◽  
Jagdish Chand Bansal ◽  
Amit Saxena ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Information ◽  
Search Algorithm ◽  
Hybrid Approach ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Parameter Analysis ◽  
Energy Curve ◽  
Remote Sensing Imagery ◽  
Renyi’S Entropy

In remote sensing imagery, segmentation techniques fail to encounter multiple regions of interest due to challenges such as dense features, low illumination, uncertainties, and noise. Consequently, exploiting vast and redundant information makes segmentation a difficult task. Existing multilevel thresholding techniques achieve low segmentation accuracy with high temporal difficulty due to the absence of spatial information. To mitigate this issue, this paper presents a new Rényi’s entropy and modified cuckoo search-based robust automatic multi-thresholding algorithm for remote sensing image analysis. In the proposed method, the modified cuckoo search algorithm is combined with Rényi’s entropy thresholding criteria to determine optimal thresholds. In the modified cuckoo search algorithm, the Lévy flight step size was modified to improve the convergence rate. An experimental analysis was conducted to validate the proposed method, both qualitatively and quantitatively against existing metaheuristic-based thresholding methods. To do this, the performance of the proposed method was intensively examined on high-dimensional remote sensing imageries. Moreover, numerical parameter analysis is presented to compare the segmented results against the gray-level co-occurrence matrix, Otsu energy curve, minimum cross entropy, and Rényi’s entropy-based thresholding. Experiments demonstrated that the proposed approach is effective and successful in attaining accurate segmentation with low time complexity.

scholarly journals A Hybrid Approach for Test Case Prioritization using PSO Based on Software Quality Metrics

2018 ◽  
Vol 7 (3.12) ◽  
pp. 300
Author(s):  
K Senthil Kumar ◽  
A Muthukumaravel
Keyword(s):  
Software Quality ◽  
Search Algorithm ◽  
Hybrid Approach ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Test Case ◽  
Test Cases ◽  
Software Application ◽  
Multiple Test ◽  
Software Quality Metrics

Effective functionality checking of any software application is the crucial event that determines the quality of outcome obtained.  Generally, checking scenarios that involves multiple test cases in mixture with multiple components is time consuming and also increases the quality assurance cost. Selection of suitable method/approach for optimization and prioritization of test cases as well as appropriate evaluation of the application would result in reduction of fault detection effort without appreciable information loss and further would also significantly decrease the clearing up cost. In the proposed method, test cases are optimized and then prioritized by Particle Swarm Optimization algorithm (PSO) and Improved Cuckoo Search algorithm (ICSA), respectively. Finally, the result will be evaluated for software quality measures. 

scholarly journals The use of Metaheuristic Algorithms in Early Prediction and Forecasting of Flood – A Use of Cuckoo Search Algorithm based Optimization for Flood Controlling

2020 ◽  
Vol 9 (2) ◽  
pp. 139-143
Keyword(s):  
Flood Control ◽  
Search Algorithm ◽  
Hybrid Approach ◽  
Cuckoo Search ◽  
Parameter Tuning ◽  
Cuckoo Search Algorithm ◽  
Optimization Techniques ◽  
Model Parameters ◽  
Early Prediction ◽  
Neural Network Models

Flood is one of the disasters which have multiple impacts on the society and industry. It has severe impacts on the urban economy and has forced the scholars to develop resiliency plans. Various types of flood forecasting techniques developed by the scholars and have certain limitations. There are various types of multiple modeling techniques which are being used for flood controlling and each has certain limitations. The optimization techniques along with the artificial intelligence algorithms can be helpful for monitoring and early prediction of flood. The neural network models promises better accuracy compared to convention models for prediction, but they face great difficulties in selection of appropriate model parameters. In the said context, here an effort has been made to explore the importance of Cuckoo theorem in flood management. The cuckoo search algorithm can be used for parameter tuning. The hybrid approach of using cuckoo search algorithm with neural networks has given far better accuracy compared to standalone algorithms. The use of such Cuckoo Search Metaheuristic algorithm will help us to predict early warning system than any other method and helps us to align the flood controlling activities. The paper presents the used of variants of cuckoo search algorithm for early flood prediction. The paper unfolds major insights of flood scenarios along with the significance of flood control and monitoring.

A Multilevel Image Thresholding Method Using the Darwinian Cuckoo Search Algorithm

2021 ◽  
pp. 2150052
Author(s):  
Ehsan Ehsaeyan ◽  
Alireza Zolghadrasli
Keyword(s):  
Image Segmentation ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Search Space ◽  
Cuckoo Search Algorithm ◽  
Computational Time ◽  
Energy Curve ◽  
Multilevel Thresholding ◽  
Curve Method ◽  
Thresholding Algorithm

Image segmentation is a prime operation to understand the content of images. Multilevel thresholding is applied in image segmentation because of its speed and accuracy. In this paper, a novel multilevel thresholding algorithm based on Cuckoo search (CS) is introduced. One of the major drawbacks of metaheuristic algorithms is the stagnation phenomenon which leads to a fall into local optimums and premature convergence. To overcome this shortcoming, the idea of Darwinian theory is incorporated with CS algorithm to increase the diversity and quality of the individuals without decreasing the convergence speed of CS algorithm. A policy of encouragement and punishment is considered to lead searching agents in the search space and reduce the computational time. The algorithm is implemented based on dividing the population into specified groups and each group tries to find a better location. Ten test images are selected to verify the ability of our algorithm using the famous energy curve method. Two popular entropies criteria, Otsu and Kapur, are employed to evaluate the capability of the introduced algorithm. Eight different search algorithms are also implemented and compared with our method. Experimental results manifest that DCS is a powerful tool for multilevel thresholding and the obtained results outperform the CS algorithm and other heuristic search methods.

Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

2020 ◽  
Vol 39 (6) ◽  
pp. 8125-8137
Author(s):  
Jackson J Christy ◽  
D Rekha ◽  
V Vijayakumar ◽  
Glaucio H.S. Carvalho
Keyword(s):  
Intelligent Transportation System ◽  
Search Algorithm ◽  
Mac Protocol ◽  
High Mobility ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Traffic Density ◽  
Scheduling Problem ◽  
Broadcast Scheduling

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

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