Authors - Emmanuel Tuyishimire Abstract - The fourth Industrial Revolution(4IR), together with the COVID-19 pandemic have made a loud call for digitizing diagnosis processes. The world is now convinced that it is imperative to digitize the diagnosis of long standing diseases such as malaria for more efficient treatment and control. It has been seen that malaria control would benefit a lot from digitising its diagnosis processes such as data gathering. We propose, in this paper, the architecture of a digital data collection system and how it is used to gather data for malaria awareness. The system is formally specified using Z notation, and based on the capability of the system, the malaria determinants are defined and their retrieving mechanisms are discussed.
Authors - Sara Sadiq Jawad, Dheyaa Jasim Kadhim Abstract - Software-defined networks (SDNs) suffer from dynamic congestion due to the nature of the data traffic they transmit. This includes both aggregated mobile network activity (such as video streaming, social media access, browsing, messaging, and mobile app usage) and real backbone internet traffic traces (which contain diverse packet flows from broadband services, cloud computing systems, downloads, server connections, and large-scale network transactions). This congestion reduces service quality and leads to poor resource allocation. Therefore, traffic prediction is considered a smart and efficient transition for SDNs. This paper proposes a deep learning-based predictive system for forecasting incoming traffic using two types of data (periodic Milano and bursty MAWI dataset). It also examines the impact of periodic and bursty traffic types on the prediction models used by the proposed system; and on the integration mechanisms used to translate predictions into actionable data. The results show that periodic Milano traffic requires temporal learning, while bursty MAWI traffic requires clipping, alignment, log scale, and robust prediction. Using MAWI traffic also required alignment between the training and evaluation phases through the application of cross-domain adaptation, unlike the Milano traffic which showed a direct response. The proposed system also demonstrated improved throughput, reduced congestion, and more stable decision-making.
Authors - Quoc-Anh Nguyen, Thanh-Nghi Doan, Huu-Hoa Nguyen Abstract - Crop productivity has been and continues to be influenced by both beneficial and harmful insect species. The classification of these insects plays a critical role in identifying threats and implementing crop protection measures. This paper presents a multimodal insect dataset for multimodal classification, utilizing both images and supplementary textual descriptions. The dataset is enriched to provide comprehensive information about various insect species. The study illustrates an integrated approach for feature extraction, similarity analysis, and insect classification. Furthermore, the research also introduces a model interpretation mechanism for the deep learning-based feature extraction process.
Authors - Petros Papagiannis, George Pallaris, Pantelitsa Leonidou Abstract - Predicting student academic performance presents a persistent challenge for higher education institutions. This paper presents a machine learning study at Cyprus College, Cyprus, using 311 studentcourse records across three semesters from 13 Computer Science courses. Four assessment components—midterm examination, final examination, assignments, and participation—alongside absence counts for 95 unique students are used as features. Seven algorithms are evaluated—Random Forest, XGBoost, SVM, Logistic Regression, K-Nearest Neighbours, Decision Tree, and Naive Bayes—using stratified five-fold cross-validation across three tasks: regression, binary pass/fail classification, and multiclass grade band prediction. SHAP analysis (applied to the full feature set) identifies feature contributions, while early-warning experiments exclude the final examination score to simulate mid-semester prediction. Results show that midterm and assignment scores predict final outcomes with R2=0.746 before the final examination, whilst Random Forest and XGBoost achieve 97.4% pass/fail accuracy. Participation contributes zero predictive signal despite a 10% grade weighting, with direct implications for assessment design at small higher education institutions.
Authors - Sukuse Abe Abstract - According to Einstein’s theory of relativity, the space we inhabit is distorted. Grigori Perelman solved the geometrization conjecture, which states that this space can be classified into eight spaces. Of these eight types, the classification of hyperbolic manifolds remains unresolved. Solving the volume conjecture would greatly advance the classification of hyperbolic manifolds. While the volume conjecture is one of the open problems in knot theory for knots in general, we successfully prove it for the family of oriented twist knots. The proof use the method of steepest descent and the theory of functions of several complex variables on the basis of colored Jones polynomials.
Authors - O. Aina, C.J. Van Staden, P. Makgato-Khunou Abstract - The role of leadership in the acceptance and integration of mobile technology for teaching and learning at a Private Higher Education Institution (PHEI) in South Africa have been explored. Mobile technologies have become widely used, but their application in higher education has been sporadic. The attitude towards mobile technology for teaching and learning are conditioned by the institutional environment which is in turn influenced by the leadership’s adoption. The study applied unified Theory of Acceptance and Use of Technology (UTAUT), Technology Acceptance Model (TAM) and Diffusion of Innovations (DOI) theories. A quantitative case study design was used with academic and institutional leaders. The study established a significant positive relationship between the construct of leadership perception and the acceptance of mobile technology for teaching and learning. The study concludes that leadership influences perceptions about acceptance of mobile technology for teaching and that enablers for the integration depend on the presence of appropriate communication channels and enabling conditions.
