2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), 2018
A Gravitational search algorithm is a physics-based heuristic algorithm inspired by Newton’s grav... more A Gravitational search algorithm is a physics-based heuristic algorithm inspired by Newton’s gravity law. GSA is good at finding the global optimum but has the drawbacks of slow convergence speed and getting stuck in local minima in last iterations. To overcome these problems, the GSA is hybridized with other swarm based optimization algorithms and it results in the increase in searching capability, problem-solving and application domains of the gravitational search algorithm. The GSA has been used to solve various optimization problems in different application areas such as clustering, classification, feature subset selection, load power dispatch, routing, etc. and it shows better performance than other swarm intelligence algorithms. This paper gives information about the GSA and its hybridization with other meta-heuristic algorithms.
A Gravitational search algorithm is a physics-based heuristic algorithm inspired by Newton's grav... more A Gravitational search algorithm is a physics-based heuristic algorithm inspired by Newton's gravity law. GSA is good at finding the global optimum but has the drawbacks of slow convergence speed and getting stuck in local minima in last iterations. To overcome these problems, the GSA is hybridized with other swarm based optimization algorithms and it results in the increase in searching capability, problem-solving and application domains of the gravitational search algorithm. The GSA has been used to solve various optimization problems in different application areas such as clustering, classification, feature subset selection, load power dispatch, routing, etc. and it shows better performance than other swarm intelligence algorithms. This paper gives information about the GSA and its hybridization with other meta-heuristic algorithms.
In recent years, various heuristic algorithms based on natural phenomena and swarm behaviors were... more In recent years, various heuristic algorithms based on natural phenomena and swarm behaviors were introduced to solve innumerable optimization problems. These optimization algorithms show better performance than conventional algorithms. Recently, the gravitational search algorithm (GSA) is proposed for optimization which is based on Newton's law of universal gravitation and laws of motion. Within a few years, GSA became popular among the research community and has been applied to various fields such as electrical science, power systems, computer science, civil and mechanical engineering, etc. This chapter shows the importance of GSA, its hybridization, and applications in solving clustering and classification problems. In clustering, GSA is hybridized with other optimization algorithms to overcome the drawbacks such as curse of dimensionality, trapping in local optima, and limited search space of conventional data clustering algorithms. GSA is also applied to classification problems for pattern recognition, feature extraction, and increasing classification accuracy.
Advances in intelligent systems and computing, Dec 12, 2018
The prospect of applying the semantic relationships to the question generation system can revolut... more The prospect of applying the semantic relationships to the question generation system can revolutionize the learning experience. The task of generating questions from the existing information is a tedious task. In this paper, Question generation system based on semantic relationships (Q-Genesis) is proposed to generate more relevant knowledge level questions automatically. It will be useful for the trainer to assess the knowledge level of the learners. This paper also provides the importance of the semantic relationships when generating the questions from the ontology.
In modern world, innovation and growth on the mobile phones are astonishing. The foundation of a ... more In modern world, innovation and growth on the mobile phones are astonishing. The foundation of a strong democracy is an informed and engaged citizenry. In many developing countries, nearly 60% of the citizenry uses mobile phones. In future, all the citizens will use mobiles in their habitual life. The wide-spread use of mobile devices has made it possible to develop mobile voting system as a complement to the existing electronic voting system. However, due to limited resource, it is challenging to achieve both efficiency and security strength for mobile voting system. Conventional system uses many symmetric and asymmetric algorithms like DES, RSA, etc to provide secure data sharing between users. Though, it is not efficient to provide security in mobile voting process. This paper proposes secure mobile voting which supports biometric identification and cryptographic algorithms to provide authentication, integrity, confidentiality and non repudiation.
2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), 2018
A Gravitational search algorithm is a physics-based heuristic algorithm inspired by Newton’s grav... more A Gravitational search algorithm is a physics-based heuristic algorithm inspired by Newton’s gravity law. GSA is good at finding the global optimum but has the drawbacks of slow convergence speed and getting stuck in local minima in last iterations. To overcome these problems, the GSA is hybridized with other swarm based optimization algorithms and it results in the increase in searching capability, problem-solving and application domains of the gravitational search algorithm. The GSA has been used to solve various optimization problems in different application areas such as clustering, classification, feature subset selection, load power dispatch, routing, etc. and it shows better performance than other swarm intelligence algorithms. This paper gives information about the GSA and its hybridization with other meta-heuristic algorithms.
A Gravitational search algorithm is a physics-based heuristic algorithm inspired by Newton's grav... more A Gravitational search algorithm is a physics-based heuristic algorithm inspired by Newton's gravity law. GSA is good at finding the global optimum but has the drawbacks of slow convergence speed and getting stuck in local minima in last iterations. To overcome these problems, the GSA is hybridized with other swarm based optimization algorithms and it results in the increase in searching capability, problem-solving and application domains of the gravitational search algorithm. The GSA has been used to solve various optimization problems in different application areas such as clustering, classification, feature subset selection, load power dispatch, routing, etc. and it shows better performance than other swarm intelligence algorithms. This paper gives information about the GSA and its hybridization with other meta-heuristic algorithms.
In recent years, various heuristic algorithms based on natural phenomena and swarm behaviors were... more In recent years, various heuristic algorithms based on natural phenomena and swarm behaviors were introduced to solve innumerable optimization problems. These optimization algorithms show better performance than conventional algorithms. Recently, the gravitational search algorithm (GSA) is proposed for optimization which is based on Newton's law of universal gravitation and laws of motion. Within a few years, GSA became popular among the research community and has been applied to various fields such as electrical science, power systems, computer science, civil and mechanical engineering, etc. This chapter shows the importance of GSA, its hybridization, and applications in solving clustering and classification problems. In clustering, GSA is hybridized with other optimization algorithms to overcome the drawbacks such as curse of dimensionality, trapping in local optima, and limited search space of conventional data clustering algorithms. GSA is also applied to classification problems for pattern recognition, feature extraction, and increasing classification accuracy.
Advances in intelligent systems and computing, Dec 12, 2018
The prospect of applying the semantic relationships to the question generation system can revolut... more The prospect of applying the semantic relationships to the question generation system can revolutionize the learning experience. The task of generating questions from the existing information is a tedious task. In this paper, Question generation system based on semantic relationships (Q-Genesis) is proposed to generate more relevant knowledge level questions automatically. It will be useful for the trainer to assess the knowledge level of the learners. This paper also provides the importance of the semantic relationships when generating the questions from the ontology.
In modern world, innovation and growth on the mobile phones are astonishing. The foundation of a ... more In modern world, innovation and growth on the mobile phones are astonishing. The foundation of a strong democracy is an informed and engaged citizenry. In many developing countries, nearly 60% of the citizenry uses mobile phones. In future, all the citizens will use mobiles in their habitual life. The wide-spread use of mobile devices has made it possible to develop mobile voting system as a complement to the existing electronic voting system. However, due to limited resource, it is challenging to achieve both efficiency and security strength for mobile voting system. Conventional system uses many symmetric and asymmetric algorithms like DES, RSA, etc to provide secure data sharing between users. Though, it is not efficient to provide security in mobile voting process. This paper proposes secure mobile voting which supports biometric identification and cryptographic algorithms to provide authentication, integrity, confidentiality and non repudiation.
Image segmentation is one of the pivotal steps in image processing. Actually, it deals with the p... more Image segmentation is one of the pivotal steps in image processing. Actually, it deals with the partitioning of the image into different classes based on pixel intensities. This work introduces a new image segmentation method based on the constriction coefficient‐based particle swarm optimization and gravitational search algorithm (CPSOGSA). The random samples of the image act as searcher agents of the CPSOGSA algorithm. The optimal number of thresholds is determined using Kapur's entropy method. The effectiveness and applicability of CPSOGSA in image segmentation is accomplished by applying it to five standard images from the USC‐SIPI image database, namely Aeroplane, Cameraman, Clock, Lena, and Pirate. Various performance metrics are employed to investigate the simulation outcomes, including optimal thresholds, standard deviation, MSE (mean square error), run time analysis, PSNR (peak signal to noise ratio), best fitness value calculation, convergence maps, segmented image graphs, and box plot analysis. Moreover, image accuracy is benchmarked by utilizing SSIM (structural similarity index measure) and FSIM (feature similarity index measure) metrics. Also, a pairwise non‐parametric signed Wilcoxon rank‐sum test is utilized for statistical verification of simulation results. In addition, the experimental outcomes of CPSOGSA are compared with eight different algorithms including standard PSO, classical GSA, PSOGSA, SCA (sine cosine algorithm), SSA (salp swarm algorithm), GWO (grey wolf optimizer), MFO (moth flame optimizer), and ABC (artificial bee colony). The simulation results clearly indicate that the hybrid CPSOGSA has successfully provided the best SSIM, FSIM, and threshold values to the benchmark images.
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