scholarly journals Elliptical curve cryptography image encryption scheme with aid of optimization technique using gravitational search algorithm

Author(s):  
Ramireddy Navatejareddy ◽  
Muthukuru Jayabhaskar ◽  
Bachala Sathyanarayana

<p>Image <span>encryption enables users to safely transmit digital photographs via a wireless medium while maintaining enhanced anonymity and validity. Numerous studies are being conducted to strengthen picture encryption systems. Elliptical curve cryptography (ECC) is an effective tool for safely transferring images and recovering them at the receiver end in asymmetric cryptosystems. This method's key generation generates a public and private key pair that is used to encrypt and decrypt a picture. They use a public key to encrypt the picture before sending it to the intended user. When the receiver receives the image, they use their private key to decrypt it. This paper proposes an ECC-dependent image encryption scheme utilizing an enhancement strategy based on the gravitational search algorithm (GSA) algorithm. The private key generation step of the ECC system uses a GSA-based optimization process to boost the efficiency of picture encryption. The image's output is used as a health attribute in the optimization phase, such as the peak signal to noise ratio (PSNR) value, which demonstrates the efficacy of the proposed approach. As comparison to the ECC method, it has been discovered that the suggested encryption scheme offers better optimal PSNR </span>values.</p>

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Sahazati Md Rozali ◽  
Mohd Fua’ad Rahmat ◽  
Abdul Rashid Husain

This paper presents backstepping controller design for tracking purpose of nonlinear system. Since the performance of the designed controller depends on the value of control parameters, gravitational search algorithm (GSA) and particle swarm optimization (PSO) techniques are used to optimise these parameters in order to achieve a predefined system performance. The performance is evaluated based on the tracking error between reference input given to the system and the system output. Then, the efficacy of the backstepping controller is verified in simulation environment under various system setup including both the system subjected to external disturbance and without disturbance. The simulation results show that backstepping with particle swarm optimization technique performs better than the similar controller with gravitational search algorithm technique in terms of output response and tracking error.


2016 ◽  
Vol 23 (2) ◽  
pp. 235-251
Author(s):  
SN Deepa ◽  
J Rizwana

The optimal location of Flexible AC Transmission Systems (FACTS) controllers in a multi-machine power system using proposed differential gravitational search algorithm (DGSA) optimization method is proposed in this paper. The main objective of this paper is to employ DGSA optimization technique to solve optimal power flow problem in the presence of Unified Power Flow controller for improving voltage profile by reducing losses along with the installation cost thereby enhancing the power system stability. A differential operator is incorporated into the gravitational search algorithm for effective search of the better solution. Due to this, the convergence and accuracy will be faster. The IEEE-6 bus, IEEE-14 bus and IEEE-30 bus systems are tested along with three other optimization techniques to validate the effectiveness of this proposed method. This proposed algorithm presents an optimal location of FACTS devices in transmission lines.


PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0253967
Author(s):  
Hazlee Azil Illias ◽  
Ming Ming Lim ◽  
Ab Halim Abu Bakar ◽  
Hazlie Mokhlis ◽  
Sanuri Ishak ◽  
...  

In power system networks, automatic fault diagnosis techniques of switchgears with high accuracy and less time consuming are important. In this work, classification of abnormal location in switchgears is proposed using hybrid gravitational search algorithm (GSA)-artificial intelligence (AI) techniques. The measurement data were obtained from ultrasound, transient earth voltage, temperature and sound sensors. The AI classifiers used include artificial neural network (ANN) and support vector machine (SVM). The performance of both classifiers was optimized by an optimization technique, GSA. The advantages of GSA classification on AI in classifying the abnormal location in switchgears are easy implementation, fast convergence and low computational cost. For performance comparison, several well-known metaheuristic techniques were also applied on the AI classifiers. From the comparison between ANN and SVM without optimization by GSA, SVM yields 2% higher accuracy than ANN. However, ANN yields slightly higher accuracy than SVM after combining with GSA, which is in the range of 97%-99% compared to 95%-97% for SVM. On the other hand, GSA-SVM converges faster than GSA-ANN. Overall, it was found that combination of both AI classifiers with GSA yields better results than several well-known metaheuristic techniques.


2020 ◽  
Vol 8 (5) ◽  
pp. 3206-3209

There is a lot of bulk data which can be efficiently structured using some Clustering mechanism, among these mechanisms Fuzzy C-Means (FCM) Clustering technique is very new and can handle this bulk data logically and in a well precise mode. FCM is a better technique when compared to K-Means as FCM is designed with Fuzzy Concerns. But clustering only cannot give precise outcome, that’s the reason we are involving an Optimization technique for tuning the results and Gravitational Search Algorithm (GSA) Optimization can makes the outcome more precise. GSA is concerned with gravity principles. GSA tailors the defects and transitions into a well structure system and finally FCM will be optimized using GSA. This System is developed with Map-Reduced method. Here in this paper, a discussion is being presented with different existing techniques that were previously used to structure the data and it is discussed how FCM with GSA is better technique when compared to those techniques and some sample Preprocessing Patterns and k-means clustering results are obtained as a first step of research.


2016 ◽  
Vol 3 (4) ◽  
pp. 1-11
Author(s):  
M. Lakshmikantha Reddy ◽  
◽  
M. Ramprasad Reddy ◽  
V.C. Veera Reddy ◽  
◽  
...  

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