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Träfflista för sökning "WFRF:(Rajan Regin) "

Search: WFRF:(Rajan Regin)

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  • Karn, Arodh Lal, et al. (author)
  • An integrated approach for sustainable development of wastewater treatment and management system using IoT in smart cities
  • 2023
  • In: Soft Computing - A Fusion of Foundations, Methodologies and Applications. - : Springer. - 1432-7643 .- 1433-7479. ; 27, s. 5159-5175
  • Journal article (peer-reviewed)abstract
    • The present world is intimidated by the problem of water scarcity that is to be addressed immediately. So, it is wise to treat wastewater to meet the massive need for drinking water for the fast-growing population. The magnificent application of Internet of Things (IoT) technology in many smart cities has derived fruitful results. This research study has proposed a real-time system using IoT that regularly monitors specific crucial parameters of a wastewater treatment plant and informs any plant's dysfunction to the operator. Furthermore, the large stream of data sets generated by IoT sensors in real-time can be analyzed and processed by complex event processing (CEP). This study was experimented with Smart Treatment (SMARTreat) architecture and its application in a simple water system of an industrial estate in South India. The proposed architecture showed outstanding results and has received positive comments from the water treatment plant managers.
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2.
  • Mehbodniya, Abolfazl, et al. (author)
  • Fetal health classification from cardiotocographic data using machine learning
  • 2021
  • In: Expert systems (Print). - : John Wiley & Sons. - 0266-4720 .- 1468-0394. ; 39:6
  • Journal article (peer-reviewed)abstract
    • Health complications during the gestation period have evolved as a global issue. These complications sometimes result in the mortality of the fetus, which is more prevalent in developing and underdeveloped countries. The genesis of machine learning (ML) algorithms in the healthcare domain have brought remarkable progress in disease diagnosis, treatment, and prognosis. This research deploys various ML algorithms to predict fetal health from the cardiotocographic (CTG) data by labelling the health state into normal, needs guarantee, and pathology. This work assesses the influence of various factors measured through CTG to predict the health state of the fetus through algorithms like support vector machine, random forest (RF), multi-layer perceptron, and K-nearest neighbours. In addition to this, the regression analysis and correlation analysis revealed the influence of the attributes on fetal health. The results of the algorithms show that RF performs better than its peers in terms of accuracy, precision, recall, F1-score, and support. This work can further enhance more promising results by performing suitable feature engineering in the CTG data.
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