7 research outputs found
EFFECTIVENESS ON CAPACITY BUILDING PROGRAMME ON BREAST SELF EXAMINATION AND KNOWLEDGE ON WARNING SIGN ON BREAST CANCER AMONG WOMEN'S
No full textAbstract: Breast cancer (BC) is the commonest malignancy among women globally. Breast cancer has ranked number one cancer among Indian females with age adjusted rate as high as 25.8 per 100,000 women and mortality 12.7 per 100,000 women. A large number of factors are identified as risk factors for breast cancer. Aim; To determine the effectiveness on capacity building programme on breast self examination and knowledge on warning sign on breast cancer among women's. Methodology: Quasi experimental research design was adopted for the current study to assess the effectiveness on capacity building programme on breast self-examination and knowledge on warning sign on breast cancer among women's at selected setting. The samples who met the inclusion criteria 60 women’s were selected by using non probability purposive sampling technique. The demographic and pretest knowledge warning sign of breast cancer and breast self-examination was collected by using a self-structured questionnaire. Followed by capacity building programme was conducted through video assisted teaching and self-breast examination demonstration performed by researcher to simulator. Post test was conducted same tools by 8th day. The Collected data were analyzed using descriptive statistics and inferential statistics. Results: The pretest mean score of knowledge was 7.72±2.43 and the posttest mean score was 17.85±2.29. The calculated paired ‘t’ test value of t=21.547 was found to be statistically highly significant at p<0.001 level. Conclusion: The capacity building programme was more effective method to create the awareness on breast self-examination and warning sign of breast cancer among all women’s.
Keywords: capacity building programme, Breast self- examination, Warning Sign, Breast cancer.
Title: EFFECTIVENESS ON CAPACITY BUILDING PROGRAMME ON BREAST SELF EXAMINATION AND KNOWLEDGE ON WARNING SIGN ON BREAST CANCER AMONG WOMEN'S
Author: Dr. G. Bhuvaneswari, S. Rashika, J. Cathrin
International Journal of Healthcare Sciences
ISSN 2348-5728 (Online)
Vol. 10, Issue 2, October 2022 - March 2023
Page No: 19-25
Research Publish Journals
Website: www.researchpublish.com
Published Date: 11-October-2022
DOI: https://doi.org/10.5281/zenodo.7185104
Paper Download Link (Source)
https://www.researchpublish.com/papers/effectiveness-on-capacity-building-programme-on-breast-self-examination-and-knowledge-on-warning-sign-on-breast-cancer-among-womensInternational Journal of Healthcare Sciences, ISSN 2348-5728 (Online), Research Publish Journals, Website: www.researchpublish.co
Advancement of corrosion inhibitor system through N-heterocyclic compounds: a review
No full textOrganic compounds containing heteroatoms have been comprehended to exhibit a remarkable tendency towards corrosion mitigation. This corrosion mitigating tendency is enabled by the presence of electron clouds composed of lone pairs, pi-electrons, which allow them to be adsorbed over metallic equivalents. Because of the availability of nitrogen as heteroatoms, these compounds are being extensively used for corrosion diminution. This article looks at a variety of heterocyclic organic compounds like imidazole, triazole, pyridine, pyrazole, quinoline, tetrazole, pyrimidine, purine and pyrrole, having nitrogen as their prime constituent heteroatom for their anti-corrosive properties. The anti-corrosive properties of these nitrogen-based compounds in acidic conditions have been demonstrated using all available experimental techniques such as EIS, PDP and other theoretical investigations such as DFT and MD. Furthermore, the shift in the trend of these compounds inhibitory efficiencies has been noted and underlined here.SERB New Delhi; Mizoram University; [SRG/2022/000152]AcknowledgementsAll the authors acknowledge their corresponding institutions for providing opportunity to carry out this research. Author MS acknowledges the financial support received from SERB New Delhi (File No. SRG/2022/000152 and research promotion grant received from Mizoram University
Optimal classifier for an ML-assisted resource allocation in wireless communications
No full textThis letter advances on the outage probability (OP) performance of a machine learning (ML)-assisted single-user multi-resource system. We focus on OP optimality and the trade-off between outage improvement and the mean number of resources scanned until a suitable resource is captured. We first present expressions for the OP of this system, complemented by an outage loss function (OLF) for its minimization. We then derive: (i) the necessary and sufficient properties of an optimal model (OpM) and (ii) expressions for the average number of resources scanned by both OpM and non-OpMs. Here, non-OpMs refer to those trained with the OLF and binary cross entropy (BCE) loss functions. We establish that optimal performance requires a channel that exhibits no time decorrelation properties. For very high decorrelation values, we find that models trained using the OLF and BCE perform similarly. For intermediate (practical) decorrelation values, OLF outperforms BCE, and both approach the OpM as decorrelation tends to zero. Our analysis further reveals that, to be able to capture a suitable resource, models trained with the OLF scan a slightly higher number of resources than the OpM and those trained with BCE. This increase in the mean number of scanned resources is offset by a significant enhancement in the OP as compared to BCE
ML-assisted resource allocation outage probability: simple, closed-form approximations
No full textIn this paper, we establish simple and efficient approximations for the outage probability of a single-user multi-resource allocation system that consists of a machine learning (ML) based outage predictor whose task is to assign resources to the user while minimizing outages. We begin by presenting the outage probability expressions for this system. We then propose the approximations to this system’s outage probability using both the sinc function and the zeroth-order Bessel function of the first kind. These approximations are based on naive upper and lower bounds and stem from understanding how the channel samples de-correlate over time. Our results demonstrate that the outage probability indeed lies within the range defined by the bounds. Moreover, the effectiveness of the proposed outage probability approximations are evident as they exhibit a strong alignment with the trend of the outage probability curve. Finally, because of the simplicity of our approximations, they can be calculated in a computationally efficient manner
Impact of Platt scaling on calibration in ML-based wireless resource allocation
No full textIn this paper, we study the calibration performance of a machine learning (ML)-based outage predictor applied to a single-user, multi-resource allocation system. Our approach models the wireless channel using Rayleigh fading with temporal characteristics that are consistent with Clarke’s 3D model. This allows us to account for the mobility of the receiver, introducing correlation between successive channel samples. Building upon this, we study the calibration performance of the outage predictor when Platt scaling is applied. Its calibration performance is assessed by generating histogram-based reliability plots with logarithmic binning. This outage predictor is trained using a customized outage loss function (OLF) as well as the commonly employed binary cross entropy. Using the negative log likelihood as an indicator, our results show that Platt scaling has a clear impact upon calibration performance, with greater enhancement observed at higher confidence levels and less improvement at lower ones. Furthermore, we observe that Platt scaling is particularly effective in minimizing overconfidence for predictors trained with OLF at lower classification thresholds and as the signal-to-noise ratio increases
Beyond linear binning: logarithmic insights for calibrated machine learning in wireless systems
No full textIn this paper, we explore the calibration of a machine learning (ML)-based outage predictor aimed at optimizing resource allocation to minimize outages in communication systems. We model the wireless channel using an auto-correlated time series with samples distributed in accordance with a Rayleigh fading process. Our novel contribution concerns proposing histogram-based reliability plots that employ logarithmic binning to assess its impact on the model calibration compared to the traditional linear binning technique. We train our ML model using an outage loss function (OLF) tailored to this system and the well-known binary cross entropy (BCE). Additionally, we analyze the effect of different model parameters on the calibration performance of this outage predictor. Our findings demonstrate that logarithmic binning reveals nuanced calibration traits ignored by linear binning, particularly at lower confidence levels. This finding is crucial for wireless systems for which understanding behavior at very low probabilities is essential. Additionally, we observe that our ML model trained with OLF becomes more overconfident as the classification threshold increases and in scenarios characterized with rare events, namely outages. This observation serves as a tool for improving the calibration properties of OLF
