Search results for: Zeleke Mekonnen

Authors: Tesfaldet Mekonnen Estifanos, Chen Hui, Afewerki Weldezgi

Abstract:

Background: Youth in general and young females in particular, remain at the center of the HIV/AIDS epidemic. Sexual risk-taking among young unmarried women is relatively high and are the most vulnerable and highly exposed to HIV/AIDS. Improvements in the status of HIV/AIDS knowledge and acceptance attitude towards people living with HIV (PLWHIV) plays a great role in averting the incidence of HIV/AIDS. Thus, the aim of the study was to explore the level and correlates of HIV/AIDS knowledge and accepting attitude toward PLWHIV. Methods: A cross-sectional study was conducted using data from the Uganda Demographic Health Survey 2016 (UDHS-2016). National level representative household surveys using a multistage cluster probability sampling method, face to face interviews with standard questionnaires were performed. Unmarried women aged 15-24 years with a sample size of 2019 were selected from the total sample of 8674 women aged 15-49 years and were analyzed using SPSS version 23. Independent variables such as age, religion, educational level, residence, and wealth index were included. Two binary outcome variables (comprehensive HIV/AIDS knowledge and acceptance attitude toward PLWHIV) were utilized. We used the chi-square test as well as multivariate regression analysis to explore correlations of explanatory variables with the outcome variables. The results were reported by odds ratios (OR) with 95% confidence interval (95% CI), taking a p-value less than 0.05 as significant. Results: Almost all (99.3%) of the unmarried women aged 15-24 years were aware of HIV/AIDS, but only 51.2% had adequate comprehensive knowledge on HIV/AIDS. Only 69.4% knew both methods: using a condom every time had sex, and having only one faithful uninfected partner can prevent HIV/AIDS transmission. About 66.6% of the unmarried women reject at least two common local misconceptions about HIV/AIDS. Moreover, an alarmingly few (20.3%) of the respondents had a positive acceptance attitude to PLWHIV. On multivariate analysis, age (20-24 years), living in urban, being educated and wealthier, were predictors of having adequate comprehensive HIV/AIDS knowledge. On the other hand, research participants with adequate comprehensive knowledge about HIV/AIDS were highly likely (OR, 1.94 95% CI, 1.52-2.46) to have a positive acceptance attitude to PLWHIV than those with inadequate knowledge. Respondents with no education, Muslim, and Pentecostal religion were emerged less likely to have a positive acceptance attitude to PLWHIV. Conclusion: This study found out the highly accepted level of awareness, but the knowledge and positive acceptance attitude are not encouraging. Thus, expanding access to comprehensive sexuality and strengthening educational campaigns on HIV/AIDS in communities, health facilities, and schools is needed with a greater focus on disadvantaged women having low educational level, poor socioeconomic status, and those residing in rural areas. Sexual risk behaviors among the most affected people - young women have also a role in the spread of HIV/AIDS. Hence, further research assessing the significant contributing factors for sexual risk-taking might have a positive impact on the fight against HIV/AIDS.

Keywords: acceptance attitude, HIV/AIDS, knowledge, unmarried women

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Authors: Bitewulign Mekonnen

Abstract:

Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

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