1. The Relationship between Computer and Mathematics Proficiency; Moderating Effects of Student Type and Location
Authors- Enti Louis Mensah, Richmond Antwi, Emmanuel
Abstract- The rapid integration of computers and digital tools in education has raised questions about the relationship between students’ computer proficiency and their academic performance, particularly in the domain of mathematics (Tamin et al., 2011; Papastergiou, 2009). Mathematics is a critical subject that underpins many areas of study and is essential for success in various fields (Mullis et al., 2016). Understanding the interplay between computer proficiency and mathematics performance is crucial for developing effective educational strategies and policies. This study aims to investigate the relationship between computer proficiency and mathematics performance, while examining the moderating effects of student type and location. The study will explore whether the strength and nature of the relationship differ based on factors such as student age, grade level, academic background, and geographical setting (e.g., urban vs. rural). By considering the moderating influences of student type and location, the research seeks to provide a comprehensive understanding of the complex relationship between computer proficiency and mathematics performance. The findings can inform the design and implementation of technology-enhanced learning environments, teacher training programs, and targeted interventions to support students’ academic success, particularly in the domain of mathematics. The results of this study have the potential to contribute to the development of evidence-based educational practices and policies that effectively leverage technology to enhance student learning and achievement in mathematics.
2. Design and Implementation of Cryptographic Data Security for IoT Devices Using Unicode & ASCII
Authors- Eberechukwunemerem John Sunday
Abstract- The study on the design and implementation of cryptographic data security for IoT devices using Unicode and ASCII is motivated by the high rate of insecurity of data observed during data transfers on IoT devices. IoT devices are one of the popular channels that assist users to communicate virtually from the comfort of their homes. Therefore, the purpose of this study is to design software for cryptographic data security that can encrypt data while in transit to another IoT device using a cipher algorithm based on symmetric encryption. Some related encryption algorithms were also reviewed. In gathering data, secondary sources were deployed for useful information. The waterfall development methodology was utilized in this work. It is a methodology where phases do not overlap and the input of the current phase is the output of the previous phase. The use of Unicode and ASCII in substitution and transposition techniques helped to achieve confidentiality, authentication, integrity, and guaranteed data security. The system was implemented using the PHP programming language, HTML, and CSS. The results obtained from this research indicate that the software outperforms other conventional encryption algorithms in terms of speed (it encrypted 148 kb of data in 0.00267 MS) and also in terms of cryptanalyst attacks such as brute-force and dictionary attacks. The software is highly recommended to individual users, network administrators, medical centers, educational institutions, and government organizations.
3. An In-Depth Examination of Student Stress Levels: A Comprehensive Analysis
Authors- Sumaiya Jamir Mulla
Abstract- This study presents a detailed analysis of student stress levels based on a comprehensive dataset gathered from undergraduate and graduate students across diverse disciplines. The dataset includes self-reported stress levels, demographic information, academic performance metrics, and factors contributing to stress. Utilizing statistical analysis techniques, including correlation analysis, regression modelling, and clustering algorithms, we uncover patterns and relationships within the dataset. Our analysis explores the impact of various factors such as academic workload, time management skills, social support networks, and coping mechanisms on student stress levels. Additionally, we investigate demographic differences in stress experiences, considering factors such as academic year. Preliminary findings reveal a complex interplay of factors influencing student stress levels. Academic workload emerges as a significant predictor of stress, with students reporting higher stress levels during peak academic periods. However, the relationship between workload and stress is moderated by factors such as time management skills and social support, highlighting the importance of individual coping strategies.
4. A Comparative Analysis of The Performance of Some Penalized Regression Techniques in The Presence of Multicollinearity
Authors- Nwuzor, Ozoemena, A.U .UDOM
Abstract- Multicollinearity is a common issue faced by statisticians and machine learning practitioners when building predictive models. This study compared the performance of some penalized re-gression techniques (Lasso, Ridge and Elastic Net) in the presence of multicollinearity using real-life datasets. The comparison was carried out in terms of the accuracy, precision, and recall scores of each technique using the root mean square error, residual sum of squares and R-square. The outcome of this study using the real-life dataset on 442 diabetes patients measured on 10 baseline predictor variables and one measure of disease progression showed that the Ridge regression performed better than Lasso and Elastic net. Comparing the results of the methods, It was observed that Ridge model performed better in comparing the performance of some penalized regression models with multicollinearity.
5. A Survey on Electronic Health Data Analysis Techniques and Features for Machine Learning
Authors- Neeraj Mishra
Abstract- The increasing adoption of electronic health records (EHRs) and digital medical imaging systems has created unprecedented opportunities to apply machine learning (ML) in healthcare. This paper presents a comprehensive review of the features of electronic healthcare data—spanning structured tabular data, unstructured clinical notes, and imaging modalities—and the ML tech-niques used to extract clinical value from them. Work has list various learning approaches, in-cluding supervised, unsupervised, and ensemble methods. As most of medical data are in images hence image features like co-occurrence matrices, wavelet transformations, edge detection, etc. were brief. This review aims to bridge the gap between technical advances in machine learning and their practical implications for modern healthcare delivery.