A Comparative Study on the Academic Performance of Students in Bachelor’s Degree of Information Technology Having Arts and Science Background in Uganda

Variations in the academic performance among students at all levels of education are one issue for years now that has attracted the attention of many researchers across the globe. This has prompted researchers and educationists to find out what factors or reasons can be attributed to these variations. Numerous studies have been conducted to determine the various reasons to explain this cause. The purpose of this study therefore, was to compare the academic performance of students in the Bachelor’s degree of Information Technology (BIT) having Arts and Science backgrounds in universities of Uganda. In order to achieve the objective of this study, a sample of 202 final year BIT students were purposively selected from two universities in Uganda. These students were categorized on the basis of their A’ level backgrounds (130 Arts and 72 Sciences). A descriptive approach employing the Welch’s t-test was used to determine the difference between the performance of the two groups and a simple linear regression analysis was used to examine the correlation among students’ performance between semesters. The results indicated that there’s a significant difference in the academic performance of the two groups, with the science group outperforming arts. However, it was found that there is a more linear increase in the performance of Arts students from semester one through semester five. Furthermore, Arts students performed slightly better than Science counterparts in some course units. Thus the study concludes that Science students perform better than Arts students in the overall semester final examination with Arts students having room for improvement in their performance

A Novel Cloud Enabled Access Control Model for Preserving the Security and Privacy of Medical Big Data

In the context of healthcare, big data refers to a complex compilation of digital medical data collected from many sources that are difficult to manage with normal technology and software due to its size and complexity. These big data are useful in various aspects of healthcare, such as disease diagnosis, early prevention of diseases, and predicting epidemics. Even though medical big data has many advantages and a lot of potential for revolutionizing healthcare, it also has a lot of drawbacks and problems, of which security and privacy are of the utmost concern, owing to the severity of the complications once the medical data is compromised. On the other hand, it is evident that existing security and privacy safeguards in healthcare organizations are insufficient to protect their massive, big data repositories and ubiquitous environment. Thus, motivated by the synthesizing of the current knowledge pertaining to the security and privacy of medical big data, including the countermeasures, in the study, firstly, we provide a comprehensive review of the security and privacy of medical big data, including countermeasures. Secondly, we propose a novel cloud-enabled hybrid access control framework for securing the medical big data in healthcare organizations, and the result of this research indicates that the proposed access control model can withstand most cyber-attacks, and it is also proven that the proposed framework can be utilized as a primary base to build secure and safe medical big data solutions. Thus, we believe this research would be useful for future researchers to comprehend the knowledge on the security and privacy of medical big data and the development of countermeasures

FCD-AttResU-Net: An improved forest change detection in Sentinel-2 satellite images using attention residual U-Net

Forest Change Detection (FCD) is a critical component of natural resource monitoring and conservation strategies, enabling informed decision-making. Various methods utilizing the power of artificial intelligence (AI) have been developed for detecting and categorizing changes in forest cover using remote sensing (RS) data. One prominent AI-powered approach is the U-Net, a deep learning (DL) architecture famous for its segmentation proficiency. However, the standard U-Net architecture fails to effectively capture intricate spatial dependencies and long-range contextual information present in remote sensing imagery. To address this research gap, we introduce an attention-residual-based novel DL model which leverages the U-Net architecture and Sentinel-2 satellite images to map alterations in forest vegetation cover in the tropical region. Our novel model enhances the U-Net architecture by seamlessly integrating the strengths of the U-Net, harnessing attention mechanisms strategically to amplify crucial features, and leveraging cutting-edge residual connections to facilitate the smooth flow of information and gradient propagation. These meticulous design choices enabled the precise feature extraction, resulting in improved computational performance of the proposed method compared to the Standard U-Net, Deeplabv3+, Deep Res-U-Net, and Attention U-Net. The classification results demonstrate the enhanced efficiency of our model, achieving a Mean Intersection over Union (MIoU) of 0.9330 on our test dataset. This performance surpasses the Attention U-Net (0.9146), Standard U-Net (0.9029), Deeplabv3+ (0.9247), and Deep Res-U-Net (0.9282). The comparative analysis of ground truth reproductions unveiled the superior detection capabilities of our model in accurately identifying forest and non-forest polygons, surpassing both the standard U-Net, and the U-Net augmented with attention mechanism, along with other state-of-the-art techniques, thereby highlighting its enhanced efficacy. The model’s broad applicability can support forest managers and ecologists in rapidly evaluating the long-term ramifications of infrastructure initiatives, such as roads, on tropical forests, including those in Brunei