Browsing by Author "Chapman-Wardy, C."
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Item Modeling the Amount of Waste Generated by Households in the Greater Accra Region Using Artificial Neural Networks(Hindawi, 2021) Chapman-Wardy, C.; Asiedu, L; Doku-Amponsah, K.; Mettle, F.O.Waste can be defined as solids or liquids unwanted by members of the society and meant to be disposed. In developing countries such as Ghana, the management of waste is the responsibility of the metropolitan authorities. These authorities do not seem to have effective management of the waste situation, and therefore, it is not unusual to see waste clog the drains and litter the streets of the capital city, Accra. )e impact of waste on the environment, along with its associated health-related problems, cannot be overemphasized. )e Joint Monitoring Programme report in 2015 ranked Ghana as the seventh dirtiest country in the world. )e lack of effective waste management planning is evident in the large amount of waste dumped in open areas and gutters that remains uncollected. In planning for solid waste management, reliable data concerning waste generation, influencing factors on waste generation, and a reliable forecast of waste quantities are required. )is study used two algorithms, namely, Lev enberg–Marquardt and the Bayesian regularization, to estimate the parameters of an artificial neural network model fitted to predict the average monthly waste generated and critically assess the factors that influence solid waste generation in some selected districts of the Greater Accra region. )e study found Bayesian regularization algorithm to be suitable with the minimum mean square error of 104.78559 on training data and 217.12465 on test data and higher correlation coefficients (0.99801 on training data, 0.99570 on test data, and 0.99767 on the overall data) between the target variables (average monthly waste generated) and the predicted outputs. House size, districts, employment category, dominant religion, and house type with respective importance of 0.56, 0.172, 0.061, 0.027, and 0.026 were found to be the top five important input variables required for forecasting household waste. It is recommended that efforts of the government and its stakeholders to reduce the amount of waste generated by households be directed at providing bins, increasing the frequency of waste collection (especially in highly populated areas), and managing the economic activities in the top five selected districts (Ledzekuku Krowor, Tema West, Asheidu Keteke, Ashaiman, and Ayawaso West), amongst others.Item Portfolio Optimization Using Minimum Variance Line Approach: A Case Study of the Social Security and National Insurance Trust(Ghana Social Science Journal, 2017-12) Nkum, N.; Lotsi, A.; Chapman-Wardy, C.; Doku-Amponsah, K.The main motive of investors around the globe is to invest in assets with the idea of maximizing the return with a minimum risk of investment. The management of investment portfolio requires carefully selecting various assets to invest in, as well as managing the proportions of funds to be channeled into a particular investment. The data used in this study was obtained from Social Security and National Investment Trust (SSNIT) covering a five-year period spanning from 2010 to 2014. The data comprised of the prices of Investment Properties (IVP), Investment to Maturity (IVM) and Loans Receivable (LR), out of which the expected return of each asset, the standard deviation (SD) of each asset, the correlation between assets and the covariance between assets are computed. The methodology used here was the minimum variance line approach proposed by Harry Markowitz. The model allowed us to assign weights to various investment classes by transposing the expected returns and risk associated with them. The result showed that, as the expected return of the portfolio increases the various percentage weight to be invested in IVP and LR increase whilst that of IVM decreases. The portfolio standard deviation or risk on the other hand also increases with increasing portfolio expected returnsItem Statistical Analysis of Public Sentiment on the Ghanaian Government: A Machine Learning Approach(Hindawi, 2021) Andoh, J.; Asiedu, L.; Lotsi, A.; Chapman-Wardy, C.Gathering public opinions on the Internet and Internet-based applications like Twitter has become popular in recent times, as it provides decision-makers with uncensored public views on products, government policies, and programs. Through natural language processing and machine learning techniques, unstructured data forms from these sources can be analyzed using traditional statistical learning. The challenge encountered in machine learning method-based sentiment classification still remains the abundant amount of data available, which makes it difficult to train the learning algorithms in feasible time. This eventually degrades the classification accuracy of the algorithms. From this assertion, the effect of training data sizes in classification tasks cannot be overemphasized. This study statistically assessed the performance of Naive Bayes, support vector machine (SVM), and random forest algorithms on sentiment text classification task. The research also investigated the optimal conditions such as varying data sizes, trees, and kernel types under which each of the respective algorithms performed best. The study collected Twitter data from Ghanaian users which contained sentiments about the Ghanaian Government. The data was preprocessed, manually labeled by the researcher, and then trained using the aforementioned algorithms. These algorithms are three of the most popular learning algorithms which have had lots of success in diverse fields. 'e Naive Bayes classifier was adjudged the best algorithm for the task as it outperformed the other two machine learning algorithms with an accuracy of 99%, F1 score of 86.51%, and Matthews correlation coefficient of 0.9906. The algorithm also performed well with increasing data sizes. 'e Naive Bayes classifier is recommended as viable for sentiment text classification, especially for text classification systems which work with Big Data.