The impact of government funding on senior high enrolment in Ghana

Successive governments, both military and civilian regimes, funded senior high school education in Ghana to increase access and improve quality since the nation attained independence on 6 March 1957. In the study reported on here we adopted a quantitative research method using secondary data from five public senior high schools in the Wa Municipality, as these schools are beneficiaries of government funding in Ghana. We used the generalised linear model to test the impact of government funding on student enrolment. The study reveals that government funding has a significant impact on increasing enrolment among girls but it is not statistically significant in increasing boys’ enrolment. Keywords: enrolment; government funding; senior high school; sustainable development goal

The Free Senior High Policy: An Appropriate Replacement to The Progressive Free Senior High Policy

Governments all over the world have been improving their educational sector through funding programs aimed at reducing the financial burden on parents, increasing access and quality to education. The government of Ghana in 2017 switched policy to a free senior high school policy to reduce poverty by finally eliminating the financial burdens parents face in paying their children’s fees. This study seeks to evaluate the appropriateness of the free senior high policy in replacing the pre-existing progressive free policy. The questionnaire survey was used to collect primary data for this study. The descriptive statistics were used in analyzing the data of this study. A total number of Two hundred (200) responses were retrieved, and out of those retrieved, 57 were females, and 143 were males. All 200 responses were usable in this study. The free senior high policy proved to put more butts on seats in helping to reduce financial burdens on parents than the pre-existing progressive free policy since nothing is paid by parents or guardians

Intelligent Rider Optimization Algorithm with Deep Learning Enabled Hyperspectral Remote Sensing Imaging Classification

Hyperspectral imaging (HSI) can be attained by the use of high resolution optical sensors and it comprises several spectral bands of the identical remote sensing target and is treated as a three-dimension (3D) dataset. Recently, deep learning (DL) techniques are gained important attention among research communities for image classification. In this aspect, this study develops an intelligent rider optimization algorithm with deep learning enabled HSI classification model, named IRODL-HSIC technique. The proposed IRODL-HSIC technique aims to categorize the different class labels of the multispectral images. Besides, the IRODL-HSIC technique applies singular value decomposition. Moreover, the ResNet-152 technique was executed as a feature extractor to generate a collection of features. Furthermore, the rider optimization algorithm with cascaded recurrent neural network (CRNN) approach is utilized for the classification process. For ensuring the enhanced performance of the IRODL-HSIC algorithm, a wide range of simulations take place utilizing the multispectral images and the outcomes are examined under different aspects. The extensive comparative study highlighted the better performance of the IRODL-HSIC technique over the recent methods