Faculty Publications

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Subject: search.filters.subject.Bio-inspired algorithms

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    A bio-inspired model to provide data security in cloud storage
    (Institute of Electrical and Electronics Engineers Inc., 2017) Hitaswi, N.; Chandrasekaran, K.
    The demand for cloud computing is increasing rapidly because of the advantages it provides to the customers like, pay as you use, self-serving, elastic, sharing of resources, ease of use, and accessibility. Due to the increase in the usage of the technology, there exists a high chance of compromising the security of the data being stored on the cloud. The major hindrance in the usage of the technology is the security concerns which accompany it. This increases the demand for a robust security mechanism to protect the data on the cloud. So as to overcome this drawback of cloud computing, encrypting the data to be stored on the cloud is one of the solutions. As part of this paper, a security mechanism to improve the security of data in cloud storage is suggested. The security mechanism used is inspired by the bio-inspired genetic algorithm. The inspiration behind the proposed security model is an amalgamation of genetic algorithm and attribute based encryption. As per the methodology proposed the data need to be encrypted before being stored on the cloud. This way the cloud service provider is unaware of the data being stored and even if the data is compromised to some third party, there is no information leakage. © 2016 IEEE.
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    Enhancing web service discovery using meta-heuristic CSO and PCA based clustering
    (Springer Verlag service@springer.de, 2018) Kotekar, S.; Kamath S․, S.
    Web service discovery is one of the crucial tasks in service-oriented applications and workflows. For a targeted objective to be achieved, it is still challenging to identify all appropriate services from a repository containing diverse service collections. To identify the most suitable services, it is necessary to capture service-specific terms that comply with its natural language documentation. Clustering available Web services as per their domain, based on functional similarities would enhance a service search engine’s ability to recommend relevant services. In this paper, we propose a novel approach for automatically categorizing the Web services available in a repository into functionally similar groups. Our proposed approach is based on the Meta-heuristic Cat Swarm Optimization (CSO) Algorithm, further optimized by Principle Component Analysis (PCA) dimension reduction technique. Results obtained by experiments show that the proposed approach was useful and enhanced the service discovery process, when compared to traditional approaches. © Springer Nature Singapore Pte Ltd. 2018.
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    A novel two-step approach for overlapping community detection in social networks
    (Springer-Verlag Wien michaela.bolli@springer.at, 2017) Sarswat, A.; Jami, V.; Guddeti, G.
    With the rapid increase in popularity of online social networks, community detection in these networks has become a key aspect of research field. Overlapping community detection is an important NP-hard problem of social network analysis. Modularity-based community detection is one of the most widely used approaches for social network analysis. However, modularity-based community detection technique may fail to resolve small-size communities. Hence, we propose a novel two-step approach for overlapping community detection in social networks. In the first step, modularity density-based hybrid meta-heuristics approach is used to find the disjoint communities and the quality of these disjoint communities can be verified using Silhouette coefficient. In the second step, the quality disjoint communities with low computation cost are used to detect overlapping nodes based on Min-Max Ratio of minimum(indegree, outdegree) to the maximum(indegree, outdegree) values of nodes. We tested the proposed algorithm based on 10 standard community quality metrics along with Silhouette score using seven standard datasets. Experimental results demonstrate that the proposed approach outperforms the current state-of-the-art works in terms of quality and scalability. © 2017, Springer-Verlag GmbH Austria.
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    A Hybrid Bio-Inspired Algorithm for Scheduling and Resource Management in Cloud Environment
    (Institute of Electrical and Electronics Engineers, 2020) Domanal, S.G.; Guddeti, R.M.R.; Buyya, R.
    In this paper, we propose a novel HYBRID Bio-Inspired algorithm for task scheduling and resource management, since it plays an important role in the cloud computing environment. Conventional scheduling algorithms such as Round Robin, First Come First Serve, Ant Colony Optimization etc. have been widely used in many cloud computing systems. Cloud receives clients tasks in a rapid rate and allocation of resources to these tasks should be handled in an intelligent manner. In this proposed work, we allocate the tasks to the virtual machines in an efficient manner using Modified Particle Swarm Optimization algorithm and then allocation / management of resources (CPU and Memory), as demanded by the tasks, is handled by proposed HYBRID Bio-Inspired algorithm (Modified PSO + Modified CSO). Experimental results demonstrate that our proposed HYBRID algorithm outperforms peer research and benchmark algorithms (ACO, MPSO, CSO, RR and Exact algorithm based on branch-and-bound technique) in terms of efficient utilization of the cloud resources, improved reliability and reduced average response time. © 2008-2012 IEEE.
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    Inverse approach using bio-inspired algorithm within Bayesian framework for the estimation of heat transfer coefficients during solidification of casting
    (American Society of Mechanical Engineers (ASME), 2020) Vishweshwara, P.S.; Gnanasekaran, N.; Arun, M.
    In any parameter estimation problem, it is desirable to obtain more information in one single experiment. However, it is difficult to achieve multiple objectives in one single experiment. The work presented in this paper is the simultaneous estimation of heat transfer coefficient parameters, latent heat, and modeling error during the solidification of Al-4.5 wt %Cu alloy with the aid of Bayesian framework as an objective function that harmoniously matches the mathematical model and measurements. A 1D transient solidification problem is considered to be the mathematical model/forward model and numerically solved to obtain temperature distribution for the known boundary and initial conditions. Genetic algorithm (GA) and particle swarm optimization (PSO) are used as an inverse approach and the estimation of unknown parameters is accomplished for both pure and noisy temperature data. The use of Bayesian framework for the estimation of unknown parameters not only provides the information about the uncertainties associated with the estimates but also there is an inherent regularization term in which the inverse problem boils down to well-posed problem thereby plethora of information is extracted with less number of measurements. Finally, the results of this work open up new prospects for the solidification problem so as to obtain a feasible solution with the present approach. © © 2020 by ASME