AN EXPLAINABLE AI-DRIVEN STACKED ENSEMBLE FRAMEWORK FOR PREDICTING UNIVERSITY STUDENTS’ ENTREPRENEURIAL POTENTIAL

ABSTRACT It is important to identify university students with authentic entrepreneurial potential, since that spurs innovation, enables self-employment, and helps the economy keep improving. Conventional statistical methods and many machine learning models either do not reflect the complex determinants of entrepreneurship or achieve very high prediction accuracy without clarifying the basis for their decisions. An

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A LIGHTWEIGHT DEEP LEARNING ARCHITECTURE FOR REAL-TIME CROP DISEASE DIAGNOSIS IN RESOURCE-CONSTRAINED RURAL INFRASTRUCTURES

Abstract Agricultural productivity across sub-Saharan Africa continues to face severe constraints from crop diseases, which account for yield losses estimated at 20–40% annually in smallholder farming systems. Automated, timely disease identification presents a viable pathway to mitigating these losses, yet existing deep learning models demand computational resources that far exceed those available in rural field

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FROM SYMBOLIC RULES TO LANGUAGE MODELS: A SYSTEMATIC LITERATURE REVIEW OF THE TECHNIQUE-LEVEL EVOLUTION OF INTELLIGENT TUTORING SYSTEMS

ABSTRACT Intelligent tutoring systems (ITS) have evolved through four technical eras: symbolic, rule-based expert systems; Bayesian and statistical student modelling; deep learning knowledge tracing; and, most recently, LLM-based conversational tutors. Existing reviews examine these eras separately, or discuss the history at a pedagogical rather than technique level, without one consistent comparative framework. Following an adapted

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INTELLIGENT TUTORING SYSTEM WITHOUT NLP, CITS AND AITS DIALOGUE: A SURVEY FOR THE PERIOD OF 2000 – 2024

ABSTRACT An Intelligent Tutoring System (ITS) is a subfield of Artificial Intelligence in education focused on teaching and learning. Teachers/lecturers are using ITS to provide customized educational materials to the students while students learn a particular domain of knowledge at their own desired time without human intervention. The main goal is to provide highly developed

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A COMPARATIVE EVALUATION OF FILTER-BASED FEATURE SELECTION TECHNIQUES FOR HEART DISEASE CLASSIFICATION

ABSTRACT Cardiovascular diseases (CVDs) are the primary cause of mortality worldwide. As such, the need for effective early prediction techniques is paramount. Machine Learning (ML) algorithms can be used to predict heart disease; however, their effectiveness greatly depends on the quality of the input features. This study investigates the use of different filter-based feature selection

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ENHANCING COMPUTER SCIENCE GRADUATES’ EMPLOYABILITY IN THE CONTEMPORARY LABOUR MARKET: A STEP TOWARDS DIGITAL INNOVATION FOR INCLUSIVE DEVELOPMENT

ABSTRACT The growing disconnect between higher education institutions’ computer science curricula and the rapidly evolving demands of modern labour markets continues to pose significant challenges to graduate employability worldwide. This paper investigates how strategic curriculum reforms, digital skill integration, industry-aligned training programmes, and adaptive pedagogical approaches can better equip computer science graduates for productive careers

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A LIGHTWEIGHT DEEP LEARNING-BASED CERVICAL IMAGE RECONSTRUCTION FRAMEWORK TO SUPPORT RELIABLE MOBILE HEALTH DIAGNOSTICS IN RESOURCE-CONSTRAINED ENVIRONMENTS

ABSTRACT This study presents a lightweight deep neural network–based framework for cervical image reconstruction to improve the reliability of mobile health (mHealth) image transmission under adverse wireless conditions. Using the AVIVA mobile application as a real-world case study, the proposed framework addresses major issues that are common in low-resource clinical settings, such as low-resolution image

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ENDOGENOUS DIGITAL TRANSFORMATION (EDT) FRAMEWORK: ADVANCING THE DIGITAL ECONOMY FOR DEVELOPING COUNTRIES

ABSTRACT The digital economy has transcended its role as a secondary sector to become the primary engine for high-quality economic growth. For developing nations, adopting a structured, data-driven growth model is no longer a luxury but a strategic necessity to avoid permanent marginalisation from the global economy. Therefore, it is imperative to understand how developing

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LINKEDLIST-BART: A DYNAMIC CHUNKING STRATEGY FOR EFFICIENT LONG-DOCUMENT SUMMARIZATION

ABSTRACT We present LinkedList-BART, an improvement to the chunk-based BART summarizing technique that dynamically manages sentence chunks using a linked-list data structure. In contrast to static chunking techniques, our method allows for efficient O(1) chunk insertion, deletion, and reorganization by representing sentence fragments as nodes in a linked list. By allowing adaptive chunk splitting or

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