DEEP LEARNING FOR OPTICAL MALARIA DIAGNOSIS: A SYSTEMATIC REVIEW OF ALGORITHMIC ADVANCES, DATASET BIASES, AND THE INDIGENOUS DATA GAP

ABSTRACT Malaria remains a leading cause of mortality in sub-Saharan Africa. While manual microscopy of Giemsa-stained blood films is the diagnostic gold standard, it is labor-intensive and prone to variability.Deep learning, particularly convolutional neural networks (CNNs), has shown strong potential for automated malaria diagnosis; however, clinical translation in African settings remains limited. This systematic review,

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MAPKET: A FRAMEWORK FOR REIMAGINING E-CAMPUS COMMERCE SYSTEM BASED ON ENTREPRENEURSHIP EDUCATION

ABSTRACT Entrepreneurship education has been introduced in Nigeria as an intervention that signposts the socio-economic development of the country. The curriculum, delivery, implementation and operation of this intervention have become a constant discourse in most extant literature. In recent times, the adoption of digital marketing and other technological advances have been discussed to improve the

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LEVERAGING MACHINE LEARNING FOR FINANCIAL INCLUSION PREDICTION IN NIGERIA: A DEVELOPMENT FINANCE APPROACH

Abstract Financial inclusion remains an essential enterprise in many developing economies, especially in sub-Saharan Africa where a large proportion of the population lacks access to formal economic services. This study presents a machine learning framework for predicting economic inclusion using demographic, social and monetary records. Three models K-Nearest Neighbors (KNN), Random Forest (RF), and an

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SECURING MACHINE-TO-MACHINE COMMUNICATION IN INDUSTRIAL IOT AND ROBOTIC NETWORKS

ABSTRACT Industrial M2M communication requires security coordination with three properties jointly unmet by existing systems: (R1) bounded-latency finality (<50 ms), (R2) multi-stakeholder governance preventing unilateral action, and (R3) resilience to intelligent insider non-reporting. This paper derives an architectural specialization of permissioned blockchain that satisfies these requirements through contextual validator eligibility and evidence-embedded transactions. Unlike prior

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A NOVEL APPROACH TO DATA RACE DETECTION USING HYBRID QUANTUM-CLASSICAL DEEP LEARNING MODELS

ABSTRACT Concurrent programming has become fundamental in modern software systems due to the widespread use of multicore processors. However, it introduces data race conditions, which remain one of the most difficult concurrency defects to detect because of their non-deterministic behaviour and complex thread interactions. Existing static and dynamic analysis techniques often suffer from high false-positive

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MODEL FOR AGRICULTURAL PRODUCTS QUALITY ASSESSMENT (AGRICQUAL)

ABSTRACT Agriculture is a vital component of Nigeria’s economy, yet the sector undergoes persistent rejection of its exports due to non-compliance with global food safety standards. This study deals with the limitations of manual, reactive quality monitoring by developing AgricQual, an intelligent decision-support system that predicts export compliance for Nigerian agricultural commodities. Adopting a Design

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DEVELOPMENT OF MARINE PREDATOR ALGORITHM (MPA) WITH MUTATION OPERATION FOR FEATURE SELECTION

ABSTRACT Several metaheuristics algorithm (MA) such as Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA) and Marine Predators Algorithm (MPA) have been considered in optimizing Feature Selection (FS) process. MPA as a recent and efficient population based MA has limitations in FS tasks with regards to its exploration/exploitation phase imbalance. This led to the development

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EFFECT OF ENSEMBLE SIZE AND TECHNIQUE ON PREDICTIVE ACCURACY OF HEPATITIS B OUTCOME: A SYSTEMATIC REVIEW

Abstract Machine Learning (ML) is an essential tool in clinical decision-making, particularly for prognosis and diagnosis. This study systematically reviews literature on ML models and ensemble learning techniques for predicting Hepatitis B virus (HBV), adhering to PRISMA guidelines. Ensemble learning combines various ML models for improved accuracy, yet concerns persist regarding the number and types

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HARNESSING AI FOR COMMUNITY HEALTH: DESIGN FRAMEWORK AND PILOT PROTOCOL FOR A HAUSA-LANGUAGE CHATBOT FOR MALARIA PREVENTION IN BAUCHI STATE, NIGERIA

ABSTRACT This paper presents a design framework and study protocol; no empirical results are reported. Malaria is a critical health burden in Bauchi State, Northern Nigeria, driven partly by the absence of communication tools accessible in Hausa, functional under low-bandwidth conditions, and suitable for low-literacy users. We propose a dual-platform AI chatbot for malaria prevention

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