Development of a new heuristic method for assembly line balancing.

Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis1975 .O43. Source: Masters Abstracts International, Volume: 40-07, page: . Thesis (M.A.Sc.)--University of Windsor (Canada), 1975

Social Media Analysis for Social Good

Data on social media is abundant and offers valuable information that can be utilised for a range of purposes. Users share their experiences and opinions on various topics, ranging from their personal life to the community and the world, in real-time. In comparison to conventional data sources, social media is cost-effective to obtain, is up-to-date and reaches a larger audience. By analysing this rich data source, it can contribute to solving societal issues and promote social impact in an equitable manner. In this thesis, I present my research in exploring innovative applications using \ac{NLP} and machine learning to identify patterns and extract actionable insights from social media data to ultimately make a positive impact on society. First, I evaluate the impact of an intervention program aimed at promoting inclusive and equitable learning opportunities for underrepresented communities using social media data. Second, I develop EmoBERT, an emotion-based variant of the BERT model, for detecting fine-grained emotions to gauge the well-being of a population during significant disease outbreaks. Third, to improve public health surveillance on social media, I demonstrate how emotions expressed in social media posts can be incorporated into health mention classification using an intermediate task fine-tuning and multi-feature fusion approach. I also propose a multi-task learning framework to model the literal meanings of disease and symptom words to enhance the classification of health mentions. Fourth, I create a new health mention dataset to address the imbalance in health data availability between developing and developed countries, providing a benchmark alternative to the traditional standards used in digital health research. Finally, I leverage the power of pretrained language models to analyse religious activities, recognised as social determinants of health, during disease outbreaks

Organizational analysis of private nursing educational institution in Karachi,Pakistan

I would like to begin with the words of Nelson Mandela, “Education is the most powerful weapon which you can use to change the world”. I strongly believe that in order to transform the coming up generation, the vision, mission and philosophy of an educational organization significantly reflects in the standards teaching and learning culture provided to their learners. In the health sector, nursing profession have undergone through a significant process of diversification from the time of Florence Nightingale. It is extremely crucial that in order to generate quality and professional nurses, educational institutions should focus on coaching the learners based on the growing burden of diseases, and equipping them with future coming up challenges

Customer relationship management (CRM) technology and organization performance: Is marketing capability a missing link? An empirical study in the Malaysian hotel industry

Due to the aggressive market competition in hotel sector, it is critical that hotels should adopt new strategy like CRM technology to assist hotel employees, serve customers better and improve organization performance.However, prior studies indicated that the relationship between CRM technology and organization performance is equivocal.These mixed results may be to a lack of understanding of the mechanisms that link CRM technology and organization performance.For this reason, the study used marketing capabilities (planning and implementation) as mediators between CRM technology and organization performance. The study surveyed a sample of 447 hotels firm in Malaysia and used correlation and regression for analyses and testing. The findings suggest that CRM technology is associated with the four dimensions of organization performance (i.e. financial, customer, internal process and learning and growth). In addition the findings reveal that marketing capabilities (planning and implementation) play a mediator role in the relationship between CRM technology and various dimensions of organization performance

Fully modified least absolute devotions and fully modified M in estimating regression model with non-stationary explanatory variable and auto correlated random errors

The research is concerned with the adoption of two robust estimation methods: The fully modified least absolute devotions method (FM-LAD) and the fully modified M method (FM-M), in estimating the parameters of regression model with non-stationary explanatory variable and autocorrelated random errors which can be modeled according to one of the mixed models, autoregressive and moving average (ARMA). The research aims to make a comparison between these two methods based on the results of their estimation using simulation experiments prepared for this purpose. The results of the simulation experiment showed the advantage of the fully modified M method over the second method depending on the trade-off criterion mean squared error (MSE)

Comparison of the two hybrid models, Wavelet-ARIMA and Wavelet-ES, to predict the prices of the US dollar index

The US dollar index is one of the most important measures to compare the value of the US dollar against a basket of foreign currencies. The strategic importance of this index lies in avoiding risks and fluctuations in the basket of major global currencies. It is known that the process of accurate prediction must take place after understanding the nature of the data of the phenomenon under study, and accordingly we can employ the most appropriate models to obtain the best predictive values. In this paper, we made a comparison between two models from the hybrid wavelet transform models, namely Wavelet-ARIMA and Wavelet-ES, by applying to data representing the weekly rates of the last price of the US dollar index from 2011 to 2022, in order to get the best predictive values for this indicator. The results of the comparison criteria AIC, RMSE and MAPE indicated the preference of the hybrid Wavelet-ARIMA model, which was used to predict the weekly rates of the index (USDX). These results indicated that there would be no significant changes or fluctuations during the next sixteen weeks, the weekly average of the index price will be ($96), the lowest predictive value of the index will be ($95.24), which will be recorded in the fourteenth week, and that the fifteenth week will record the highest predictive value of the index, as it will amount to ($96.31)

Volatility forecasting with the wavelet transformation algorithm GARCH model: Evidence from African stock markets

The daily returns of four African countries' stock market indices for the period January 2, 2000, to December 31, 2014, were employed to compare the GARCH(1,1) model and a newly proposed Maximal Overlap Discreet Wavelet Transform (MODWT)- GARCH(1,1) model. The results showed that although both models fit the returns data well, the forecast produced by the GARCH(1,1) model underestimates the observed returns whereas the newly proposed MODWT-GARCH(1,1) model generates an accurate forecast value of the observed returns. The results generally showed that the newly proposed MODWT-GARCH(1,1) model best fits returns series for these African countries. Hence the proposed MODWT-GARCH should be applied on other context to further verify its validity

Comparison of forecasting performance between MODWT-GARCH(1,1) and MODWT-EGARCH(1,1) models: Evidence from African stock markets

Many researchers documented that if stock markets' returns series are significantly skewed, linear-GARCH(1,1) grossly underestimates the forecast values of the returns. However, this study showed that the linear Maximal Overlap Discreet Wavelet Transform MODWT-GARCH(1,1) actually gives an accurate forecast value of the returns. The study used the daily returns of four African countries' stock market indices for the period January 2, 2000, to December 31, 2014. The Maximal Overlap Discreet Wavelet Transform-GARCH(1,1) model and the Maximal Overlap Discreet Wavelet Transform-EGARCH(1,1) model are exhaustively compared. The results show that although both models fit the returns data well, the forecast produced by the Maximal Overlap Discreet Wavelet Transform-EGARCH(1,1) model actually underestimates the observed returns whereas the Maximal Overlap Discreet Wavelet Transform-GARCH(1,1) model generates an accurate forecast value of the observed returns

RECURRENT HYDATIDIFORM MOLE COMPLICATED BY TOXIC GOITRE.

Hyperthyroidism complicates few pregnancies, in many cases due to Graves' disease, Gestational trophoblastic disease is a rare cause of hyperthyroidism in which high levels of hCG causes activation of the thyrotrophin receptor to stimulate supraphysiological secretion of thyroid hormone with or without thyroid gland enlargement Molar pregnancies are usually not recurrent, however, women with a previous hydatidiform mole (HM) are at higher risk of having a second mole than women from the general population. After a prior molar pregnancy, the risk of having a second one is 540 times that of the general population, however familial molar pregnancies are exceedingly rare. Here we present a case of recurrent HM complicated by a toxic goiter in a patient with family history of molar pregnancy&nbsp