Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Multi-ENPS simulator support tool with automatic file inter-conversion and multi-membrane execution(Elsevier Ireland Ltd, 2020) Raghavan, S.; Gangadhar, Y.; Pattar, V.; Chandrasekaran, K.P System or Membrane Computing is an unconventional and natural computing model inspired by the functioning of a living cell. This model has an inherently parallel structure. There are several variants of P System developed, each of which has a different application. One of the variants, Enzymatic Numerical P System (ENPS), has primarily been developed to be used with numerical values (as in economics) and thus has vast applications. For realizing ENPS there are several tools available, primarily based on Java and Python, each of which has a different input format. Currently, there is no tool which allows the user to execute ENPS using both the simulators on the same platform, the issue being inter-conversion between input formats, namely, XML and PeP (specific format designed for Python based ENPS). Another major issue with existing simulators is their inability to allow multiple membrane systems to be executed and there is no facility for interconnection between two membrane systems. A tool developed here solves both problems namely, file inter-conversion and multiple membrane support by transferring dependent variable values automatically according to users’ choice. The tool is developed using Python 3.0 and has only a few dependencies. The tool is tested under different scenarios and the results confirm the correctness of the tool. © 2019 Elsevier B.V.Item GPUPeP: Parallel Enzymatic Numerical P System simulator with a Python-based interface(Elsevier Ireland Ltd, 2020) Raghavan, S.; Rai, S.S.; Rohit, M.P.; Chandrasekaran, K.Membrane computing is a computational paradigm inspired by the structure and behavior of a living cell. P Systems are the computing devices that are used to realize membrane computing models. Numerous theoretical studies on many variants of P Systems have shown them to be computationally universal. There is a wide range of applications of P Systems from modeling of biological processes to image processing. Among many variants of P Systems, one of the most important is Enzymatic Numerical P System (ENPS). ENPS is a class of P System in which membranes operate on numerical values. To realize the power of ENPS there are a few simulators developed. Each and every simulator has some advantages as well as some disadvantages. Here, a GPU based simulator using Python as a user interaction language is developed. This tool is a completely parallel variant, compatible with a Python based sequential simulator (PeP) which was the first Python based work for ENPS. The developed simulator uses CUDA to interact with GPU and gives the desired speed up, while processing the membranes. There are two important case studies which show the performance of the developed tool to be far better than the other serial simulators. © 2020 Elsevier B.V.Item Membrane-based models for service selection in cloud(Elsevier Inc., 2021) Raghavan, S.; Chandrasekaran, K.Cloud service selection is one of the prime areas of research within the ambit of cloud computing, which has gained wide attention in the recent past. The service selection algorithm primarily involves selecting the best service from a set of available services, based on Quality of Service (QoS) attributes. The QoS attributes are the parameters which allow the users to ascertain the actual quality of the service, often quantitatively. Over the years, there have been several methods designed for service selection in the cloud that are primarily sequential, with many being sensitive to changes. Thus, the aim is to propose multiple robust and parallel models for cloud service selection. The proposed models are designed using Membrane Computing paradigm, which is an inherently parallel computing model, combined with the Improved Technique for Order of Preference by Similarity to Ideal Solution (ITOPSIS), a popular Multi-Criteria Decision Making Method. Two methods based on a tactical amalgamation of ITOPSIS and Enzymatic Numerical P System (A membrane computing device variant) structure are proposed here. The proposed parallel models are implemented, tested, and the obtained results are analyzed. The results indicate one model to be robust (less sensitive) and the other to be moderately sensitive. © 2020 Elsevier Inc.Item Ensemble deep neural network based quality of service prediction for cloud service recommendation(Elsevier B.V., 2021) Sahu, P.; Raghavan, S.; Chandrasekaran, K.Applications of Cloud Services are increasing day by day, and so is the difficulty of choosing the best-suited service for a customer. Quality of Service (QoS) parameters can be used for quality assurance and evaluation; further, a service can be recommended based on these QoS parameters’ values. Recommendation systems are getting much attention lately. It has a crucial role in almost all the major commercial platforms and many improvements are being made to make the recommendations more precise and closer to the user's requirements. Conventional Machine Learning algorithms and statistical analysis methods, presently are not that efficient in learning the complex correlation between data elements. Lately, Deep Learning models have proven to be practical and precise in areas like natural language processing, image processing, data mining, & data interpretation. However, there are not many examples of complete Deep Learning applications for cloud service recommendation systems, though some works partially use Deep Learning. We propose the Ensemble of Deep Neural Networks (EDNN) method, which is of the hybrid type, i.e., the fusion of neighborhood-based and neural network model-based methods. The output obtained from both the models are combined using another different neural network model. Our approach for predicting QoS values is simple and different from previous works, and the results show that it outperforms other classical methods marginally. © 2021 Elsevier B.V.Item ENPS-IPROMETHEE: Enzymatic Numerical P System-based Improved Preference Ranking Organization Method for Enrichment Evaluation(Springer, 2022) Raghavan, S.; Chandrasekaran, K.Membrane Computing is a natural computing paradigm inspired by the structure and activity of a biological cell. Membrane-based models can be realized using P System and these models have multiple applications. Here, it is applied to solve a Multi-criteria Decision-Making (MCDM) problem. MCDM is one of the important area in Decision-Making. It involves ranking items from a given set of items based on multiple criteria and it has several applications in different broad arenas which include Economics, Engineering and Management. MCDM includes several clusters of techniques that have been divided based on its modes of operation. All the techniques available till now consider sequential computing paradigm as the base for computation but in this work a parallel technique is used. Here, Enzymatic Numerical P System (ENPS)-based MCDM technique is designed. ENPS is a variant of P System used specifically for numerical problems. The proposed model, ENPS-IPROMETHEE is based on Improved Preference Ranking Organization Method for Enrichment Evaluation (IPROMETHEE), a popular outranking-based MCDM method. The designed model is verified and tested using PeP and GPUPeP simulators which are used for simulating ENPS models. A membrane file generator tool called as P-Generator is developed for automatic membrane generation. Two standard, existing datasets are considered and the model is studied for its sensitivity. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.