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Brindha, P.
- A Study to Assess the Effectiveness of 25% Sucrose Orally on Painful Procedure among Neonates in NICU at Mehta Children’s Hospital, Chennai, India
Authors
Source
International Journal of Innovative Research and Development, Vol 4, No 1 (2015), Pagination:Abstract
Many neonates admitted to hospital undergo repeated invasive procedures. Oral sucrose is frequently given to relieve procedural pain in neonates on the basis of its effect on behavioural and physiological pain scores. The purpose of the study is to assess the level of pain among neonates before and after administration of 25% sucrose, to assess the effectiveness of 25% sucrose orally among neonates in NICU and to assess the association between the level of pain among neonates and selected demographic variable such as age, sex, weight, gestation, ordinal position of the neonates and type of invasive procedure. The study method was Evaluate research approach and a pre experimental (one group pre-testpost-test design) was used. Non randomized purposive sampling technique was used to select the sample for the study. The total sample consists of 60 neonates admitted in NICU. Pain was assessed during invasive procedure before administration of 25%sucrose and 0.02- 2ml sucrose was given 2mins prior to next painful procedure. The pain was assessed during procedure at 30second and at 1minute by neonatal infant pain scale (NIPS) for neonates. The result of the study concluded that mean and standard deviation score for pain during procedure, 30 seconds and 1 minute in the pre-test was 6.5, 4.7, 2.5 and in the post test were 4.2, 1.9, 0.5. Thecalculated‘t’ value was 9.4***, 10.6***, 7.4*** is significant p value (<0.001). It reveals that there was significant difference existing between pre-test and post-test pain score. It is evident that the 25% sucrose is significantly effective in reducing of pain among neonates undergoing painful procedure.
- Novel Region Specific Decision Support System for Crop Selection and Cultivation
Authors
1 Department of Computer Science, Mother Teresa Women’s University, IN
Source
ICTACT Journal on Soft Computing, Vol 10, No 4 (2020), Pagination: 2134-2138Abstract
The prime concern of any country is Agriculture. Every nation has to feed its population by making strong policy support for Agricultural production. This paper deals with providing decision support for the crop to be selected for cultivation based on several influencing parameters. Even though there are many decision support systems available for Agriculture, there is a lack in region specific ones. The proposed system aims to overcome the aforementioned issue. For this purpose, the system considers climatic data from the Government of India web portal, Tamil Nadu Agricultural University portal and reports. The precision data collected from the fields will be given as inputs to the proposed system. The crops to be selected for cultivation are based on the historical data and guidelines from the Tamil Nadu Agritech portal. The accuracy of the proposed decision support system is assessed by getting feedback from the farmers.Keywords
Accuracy, Agriculture, Crop Selection, Decision Support System, Historical Data.References
- R. Wanjari, K.G. Mandal, P. Ghosh, T. Adhikari and N.H. Rao, “Rice in India: Present Status and Strategies to Boost Its Production Through Hybrids”, Journal of Sustainable Agriculture, Vol. 28, No. 2, pp. 19-39, 2006.
- K. Thiyagarajan and R. Kalaiyarasi, “Status Paper on Rice in Tamilnadu”, Rice Knowledge Management Portal Publisher, 2010.
- R. Rupnik, M. Kukar, P. Vracar and Z. Bosnic, “AgroDSS: A Decision Support System for Agriculture and Farming”, Computers and Electronics in Agriculture, Vol. 161, pp. 260-271, 2019.
- G. Yogeswari and A. Padmapriya, “Recommender System for Nutrient Management Based on Precision Agriculture”, International Journal of Recent Technology and Engineering, Vol. 8, No. 4, pp. 1-12, 2019.
- Claudio Stockle, Marcello Donatelli and Roger Nelson, “CropSyst, A Cropping Systems Simulation Model”, European Journal of Agronomy, Vol. 18, No. 3-4, pp. 289-307, 2003.
- G. Yogeswari and A. Padmapriya, “Precision Data Acquisition and Analysis for Nutrient Management of Tomatoes”, Asian Journal of Computer Science and Technology, Vol. 8, No. 2, pp. 20-23, 2019.
- J. Lindblom and M. Ljung, “Next Generation Decision Support Systems for Farmers: Sustainable Agriculture through Sustainable IT”, Proceedings of 11th European Symposium on International Farming Systems Association, pp. 1-6, 2014.
- R. Ruba Mangala and A. Padmapriya, “Visualizing the Impact of Climatic Changes on Pest and Disease Infestation in Rice”, International Journal of Recent Technology and Engineering, Vol. 8, No. 3, pp. 8413-8421, 2019.
- M. Abedinpour, A. Sarangi, T.B.S. Rajput, M. Singh and T. Ahmad, “Performance Evaluation of Aqua Crop Model for Maize Crop in a Semi-Arid Environment”, Agricultural Water Management, Vol. 110, pp. 55-66, 2012.
- R.R. Mangala and A. Padmapriya, “Prediction Based Agro Advisory System for Crop Protection”, Proceedings of International Conference on Intelligent Data Communication Technologies and Internet of Things, pp. 1-7, 2019.
- Venkatalakshmi Balakrishnan, “Decision Support System for Precision Agriculture”, International Journal of Research in Engineering and Technology, Vol. 3, No. 19, pp. 849-852, 2014.
- Guidelines for Rice, Available at: http://www.knowledgebank.irri.org/decision-tools/rice-doctor, Accessed on 2020.
- Production and Irrigation Statistics, Available at: https://visualize.data.gov.in/all_visualization, Accessed on 2020.
- Water Related Data, Available at: https://www.indiawaterportal.org/datafinder, Accessed on 2020.
- Paddy Expert Systems, Available at: http://agritech.tnau.ac.in/expert_system/paddy/Index.html, Accessed on 2020.
- Cropstaticsrice, Available at: https://www.farmer.gov.in/cropstaticsrice.aspx, Accessed on 2020.
- Yaser Sakkaf, “Decision Trees: ID2 Algorithm Explained”, Available at: https://towardsdatascience.com/decision-trees-for-classification-id3-algorithm-explained-89df76e72df1, Accessed on 2020.