Search Results - "Somu, Nivethitha"

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  1. 1

    Evaluation of building energy demand forecast models using multi-attribute decision making approach by Somu, Nivethitha, Kowli, Anupama

    Published in Energy and built environment (01-06-2024)
    “…•GI-MARCOS, decision making model for building energy demand analysis is presented.•Gini index assigns objective weight to attributes in each phase of…”
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    Journal Article
  2. 2

    A computational model for ranking cloud service providers using hypergraph based techniques by Somu, Nivethitha, Kirthivasan, Kannan, V.S., Shankar Sriram

    Published in Future generation computer systems (01-03-2017)
    “…In a cloud marketplace, the existence of wide range of Cloud Service Providers (CSPs) makes it hard for the Cloud Users (CUs) to find an appropriate CSP based…”
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    Journal Article
  3. 3

    Occupancy estimation with environmental sensors: The possibilities and limitations by Chitnis, Shubham, Somu, Nivethitha, Kowli, Anupama

    Published in Energy and built environment (01-02-2025)
    “…•Occupancy estimation modelled as classification and regression problem.•Environmental sensor data is useful for detecting presence/absence of occupants.•ML…”
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  4. 4

    An efficient intrusion detection technique based on support vector machine and improved binary gravitational search algorithm by Gauthama Raman, M. R., Somu, Nivethitha, Jagarapu, Sahruday, Manghnani, Tina, Selvam, Thirumaran, Krithivasan, Kannan, Shankar Sriram, V. S.

    Published in The Artificial intelligence review (01-06-2020)
    “…‘Curse of Dimensionality’ and the trade-off between high detection rate and less false alarm rate make the design of an efficient and robust Intrusion…”
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  5. 5

    A deep learning framework for building energy consumption forecast by Somu, Nivethitha, Raman M R, Gauthama, Ramamritham, Krithi

    Published in Renewable & sustainable energy reviews (01-03-2021)
    “…Increasing global building energy demand, with the related economic and environmental impact, upsurges the need for the design of reliable energy demand…”
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  6. 6

    A hybrid model for building energy consumption forecasting using long short term memory networks by Somu, Nivethitha, M R, Gauthama Raman, Ramamritham, Krithi

    Published in Applied energy (01-03-2020)
    “…•eDemand, a data driven energy consumption forecasting model is developed.•The proposed model is designed for accurate short, mid, and long term…”
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  7. 7

    A hybrid deep transfer learning strategy for thermal comfort prediction in buildings by Somu, Nivethitha, Sriram, Anirudh, Kowli, Anupama, Ramamritham, Krithi

    Published in Building and environment (15-10-2021)
    “…Since the thermal condition of living spaces affects the occupants' productivity and their quality of life, it is important to design effective heating,…”
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  8. 8

    IIVIFS-WASPAS: An integrated Multi-Criteria Decision-Making perspective for cloud service provider selection by Gireesha, Obulaporam, Somu, Nivethitha, Krithivasan, Kannan, V.S., Shankar Sriram

    Published in Future generation computer systems (01-02-2020)
    “…Cloud service provider selection, an important Multi-Criteria Decision Making (MCDM) problem involves intrinsic relations among the multiple alternatives,…”
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    Journal Article
  9. 9

    Multi-sensor data fusion framework for energy optimization in smart homes by Dasappa, Nirupam Sannagowdara, Kumar G, Krishna, Somu, Nivethitha

    Published in Renewable & sustainable energy reviews (01-04-2024)
    “…Advancements in Internet of Energy (IoE) technologies drive the development of several energy efficient frameworks for better energy optimization, economic…”
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    Journal Article
  10. 10

    An efficient intrusion detection system based on hypergraph - Genetic algorithm for parameter optimization and feature selection in support vector machine by Gauthama Raman, M.R., Somu, Nivethitha, Kirthivasan, Kannan, Liscano, Ramiro, Shankar Sriram, V.S.

    Published in Knowledge-based systems (15-10-2017)
    “…Realization of the importance for advanced tool and techniques to secure the network infrastructure from the security risks has led to the development of many…”
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    Journal Article
  11. 11

    A Hypergraph and Arithmetic Residue-based Probabilistic Neural Network for classification in Intrusion Detection Systems by Raman, M.R. Gauthama, Somu, Nivethitha, Kirthivasan, Kannan, Sriram, V.S. Shankar

    Published in Neural networks (01-08-2017)
    “…Over the past few decades, the design of an intelligent Intrusion Detection System (IDS) remains an open challenge to the research community. Continuous…”
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  12. 12

    A trust centric optimal service ranking approach for cloud service selection by Somu, Nivethitha, M.R., Gauthama Raman, Kirthivasan, Kannan, V.S., Shankar Sriram

    Published in Future generation computer systems (01-09-2018)
    “…Cloud service selection, a promising research directive provides an intelligent solution via. service ranking based on the Quality of Service (QoS) attributes…”
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  13. 13

    A multilayer perceptron model for anomaly detection in water treatment plants by Raman MR, Gauthama, Somu, Nivethitha, Mathur, A.P.

    “…•A multi-layer perceptron model is proposed for anomaly detection in industrial control systems.•The hyperparameters of the MLP were fine-tuned to achieve…”
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  14. 14

    An improved robust heteroscedastic probabilistic neural network based trust prediction approach for cloud service selection by Somu, Nivethitha, M.R., Gauthama Raman, V., Kalpana, Kirthivasan, Kannan, V.S., Shankar Sriram

    Published in Neural networks (01-12-2018)
    “…Trustworthiness is a comprehensive quality metric which is used to assess the quality of the services in service-oriented environments. However, trust…”
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  15. 15

    A rough set-based hypergraph trust measure parameter selection technique for cloud service selection by Somu, Nivethitha, Kirthivasan, Kannan, Shankar Sriram, V. S.

    Published in The Journal of supercomputing (01-10-2017)
    “…Selection of trustworthy cloud services has been a major research challenge in cloud computing, due to the proliferation of numerous cloud service providers…”
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  16. 16

    IBGSS: An Improved Binary Gravitational Search Algorithm based search strategy for QoS and ranking prediction in cloud environments by Somu, Nivethitha, M.R., Gauthama Raman, Kaveri, Akshya, K., Akshay Rahul, Krithivasan, Kannan, V.S., Shankar Sriram

    Published in Applied soft computing (01-03-2020)
    “…Quality of Service (QoS) value prediction and QoS ranking prediction have their significance in optimal service selection and service composition problems. QoS…”
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  17. 17

    A hybrid approach using rough set theory and hypergraph for feature selection on high-dimensional medical datasets by Gauthama Raman, M. R., Nivethitha, Somu, Kannan, Krithivasan, Shankar Sriram, V. S.

    Published in Soft computing (Berlin, Germany) (01-12-2019)
    “…‘Curse of Dimensionality’—massive generation of high-dimensional medical datasets from various biomedical applications hardens the data analytic process for…”
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  18. 18

    An improved rough set approach for optimal trust measure parameter selection in cloud environments by Nivethitha, Somu, Gauthama Raman, M. R., Gireesha, Obulaporam, Kannan, Krithivasan, Shankar Sriram, V. S.

    Published in Soft computing (Berlin, Germany) (01-11-2019)
    “…The existence of a multitude of cloud service providers (CSPs) for each service type increases the difficulty in the identification of appropriate and…”
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  19. 19

    Hypergraph Based Feature Selection Technique for Medical Diagnosis by Somu, Nivethitha, Raman, M. R. Gauthama, Kirthivasan, Kannan, Sriram, V. S. Shankar

    Published in Journal of medical systems (01-11-2016)
    “…The impact of internet and information systems across various domains have resulted in substantial generation of multidimensional datasets. The use of data…”
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  20. 20

    EnsembleNTLDetect: An Intelligent Framework for Electricity Theft Detection in Smart Grid by Kulkarni, Yogesh, Z, Sayf Hussain, Ramamritham, Krithi, Somu, Nivethitha

    Published 09-10-2021
    “…Artificial intelligence-based techniques applied to the electricity consumption data generated from the smart grid prove to be an effective solution in…”
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