scholarly journals A New Reversible Database Watermarking Approach with Firefly Optimization Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Mustafa Bilgehan Imamoglu ◽  
Mustafa Ulutas ◽  
Guzin Ulutas
Keyword(s):  
Relational Databases ◽  
Data Centers ◽  
Optimization Technique ◽  
Access Network ◽  
Public Access ◽  
Difference Expansion ◽  
Digital Rights ◽  
Firefly Optimization ◽  
Embed Watermark ◽  
Firefly Optimization Algorithm

Up-to-date information is crucial in many fields such as medicine, science, and stock market, where data should be distributed to clients from a centralized database. Shared databases are usually stored in data centers where they are distributed over insecure public access network, the Internet. Sharing may result in a number of problems such as unauthorized copies, alteration of data, and distribution to unauthorized people for reuse. Researchers proposed using watermarking to prevent problems and claim digital rights. Many methods are proposed recently to watermark databases to protect digital rights of owners. Particularly, optimization based watermarking techniques draw attention, which results in lower distortion and improved watermark capacity. Difference expansion watermarking (DEW) with Firefly Algorithm (FFA), a bioinspired optimization technique, is proposed to embed watermark into relational databases in this work. Best attribute values to yield lower distortion and increased watermark capacity are selected efficiently by the FFA. Experimental results indicate that FFA has reduced complexity and results in less distortion and improved watermark capacity compared to similar works reported in the literature.

scholarly journals Firefly optimization technique based test scenario generation and prioritization

2018 ◽  
Vol 16 (6) ◽  
Author(s):  
Vikas Panthi ◽  
Durga Prasad Mohapatra
Keyword(s):  
Optimization Technique ◽  
State Machine ◽  
Scenario Generation ◽  
Test Scenario ◽  
Average Percentage ◽  
Model Based Testing ◽  
Composite Models ◽  
Firefly Optimization ◽  
Test Scenarios ◽  
Firefly Optimization Algorithm

Model-based testing shows a significant role-play in the area of software testing. This paper presents a new automatic test scenarios generation technique using UML state machine diagram having composite states. The intention of this research is to generate test scenarios for concurrent and composite states in state machines using the proposed algorithm SMToTSG (State Machine To Test Scenarios Generation). We have prioritized the test scenarios using Firefly optimization algorithm. We have used state-based coverage criteria such as state, transition, transition pair coverage to evaluate the efficiency of the proposed algorithm. The proposed approach is useful for feasible test scenario generation. Generating exhaustive test scenarios for all concurrent interdependent sequences is very difficult. In this paper, we generate the important test scenarios in the presence of concurrency in composite models. After prioritization, we apply Average Percentage Fault Detection (APFD) metric to calculate the efficiency of the prioritized test scenarios.  

Array Design for the Synthesis of Stair Step Patterns Using Firefly Optimization Technique

2019 ◽  
Vol 9 (4) ◽  
pp. 285
Author(s):  
Krishna Rudraraju Chaitanya ◽  
P. Mallikarjuna Rao ◽  
K. V. S. N. Raju ◽  
G. S. N. Raju
Keyword(s):  
Optimization Technique ◽  
Array Design ◽  
Firefly Optimization

scholarly journals BUILDING ONTOLOGIES OVER RELATIONAL DATABASES

2018 ◽  
Vol 6 (11) ◽  
pp. 254-265
Author(s):  
Damitha D Karunaratna
Keyword(s):  
Relational Databases ◽  
World Wide ◽  
Query Languages ◽  
Public Access ◽  
Prototype System ◽  
Data Bases ◽  
Domain Specific ◽  
The World ◽  
Access Data ◽  
Relational Data Bases

Relational Databases are typically created to fulfil the information requirements of a community of users generally belongs to a single organization. Data stored in these databases were typically accessed by using Structured Query Languages or through customized interfaces.  With the popularity of the World Wide Web and the availability of large number of Relational Databases for public access there is a need for users to retrieve data from these databases by using a text-based queries, possibly by using the terms that they are familiar with. However, the inherent limitations of Structured Query Languages used to create and access data in relational Data Bases does not allow uses to access data by using text-based queries. Also, the terms used in queries should be limited to those used during the construction of the databases. This paper proposes an architecture to generated ontologies over relation databases and show how they could be enhanced semantically by using available domain-specific or top-level ontologies so that the data managed by the DBs can be accessed by using text-based queries. The feasibility of the proposed architecture was demonstrated by building a prototype system over a sample MySQL database.

Evaluation of the trust values among human resources in the enterprise cloud using an optimization algorithm and fuzzy logic

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lantian Li ◽  
Bahareh Pahlevanzadeh
Keyword(s):  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Human Life ◽  
Cloud Services ◽  
Cloud Provider ◽  
Suggested Approach ◽  
Content Type ◽  
Firefly Optimization ◽  
Security Costs ◽  
Firefly Optimization Algorithm

PurposeCloud eases information processing, but it holds numerous risks, including hacking and confidentiality problems. It puts businesses at risk in terms of data security and compliance. This paper aims to maximize the covered human resource (HR) vulnerabilities and minimize the security costs in the enterprise cloud using a fuzzy-based method and firefly optimization algorithm.Design/methodology/approachCloud computing provides a platform to improve the quality and availability of IT resources. It changes the way people communicate and conduct their businesses. However, some security concerns continue to derail the expansion of cloud-based systems into all parts of human life. Enterprise cloud security is a vital component in ensuring the long-term stability of cloud technology by instilling trust. In this paper, a fuzzy-based method and firefly optimization algorithm are suggested for optimizing HR vulnerabilities while mitigating security expenses in organizational cloud environments. MATLAB is employed as a simulation tool to assess the efficiency of the suggested recommendation algorithm. The suggested approach is based on the firefly algorithm (FA) since it is swift and reduces randomization throughout the lookup for an optimal solution, resulting in improved performance.FindingsThe fuzzy-based method and FA unveil better performance than existing met heuristic algorithms. Using a simulation, all the results are verified. The study findings showed that this method could simulate complex and dynamic security problems in cloud services.Practical implicationsThe findings may be utilized to assist the cloud provider or tenant of the cloud infrastructure system in taking appropriate risk mitigation steps.Originality/valueUsing a fuzzy-based method and FA to maximize the covered HR vulnerabilities and minimize the security costs in the enterprise cloud is the main novelty of this paper.

Adaptive Optimization-Enabled Neural Networks to Handle the Imbalance Churn Data in Churn Prediction

2021 ◽  
Author(s):  
Bharathi Garimella ◽  
G. V. S. N. R. V. Prasad ◽  
M. H. M. Krishna Prasad
Keyword(s):  
Prediction Method ◽  
Training Data ◽  
Jaccard Coefficient ◽  
Master Node ◽  
Churn Prediction ◽  
Dice Coefficient ◽  
Adaptive Optimization ◽  
Firefly Optimization ◽  
Spark Framework ◽  
Firefly Optimization Algorithm

The churn prediction based on telecom data has been paid great attention because of the increasing the number telecom providers, but due to inconsistent data, sparsity, and hugeness, the churn prediction becomes complicated and challenging. Hence, an effective and optimal prediction of churns mechanism, named adaptive firefly-spider optimization (adaptive FSO) algorithm, is proposed in this research to predict the churns using the telecom data. The proposed churn prediction method uses telecom data, which is the trending domain of research in predicting the churns; hence, the classification accuracy is increased. However, the proposed adaptive FSO algorithm is designed by integrating the spider monkey optimization (SMO), firefly optimization algorithm (FA), and the adaptive concept. The input data is initially given to the master node of the spark framework. The feature selection is carried out using Kendall’s correlation to select the appropriate features for further processing. Then, the selected unique features are given to the master node to perform churn prediction. Here, the churn prediction is made using a deep convolutional neural network (DCNN), which is trained by the proposed adaptive FSO algorithm. Moreover, the developed model obtained better performance using the metrics, like dice coefficient, accuracy, and Jaccard coefficient by varying the training data percentage and selected features. Thus, the proposed adaptive FSO-based DCNN showed improved results with a dice coefficient of 99.76%, accuracy of 98.65%, Jaccard coefficient of 99.52%.

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