Koforidua Technical University

Anemia has become one of the global public health burdens that distracts the well-being of people and affects billions of people across the world. Anemia occurs when the hemoglobin level within the body is reduced below the normal threshold, or when there is a deficiency in the number of blood cells the body produces. The clinical method for anemia diagnosis is centered on the invasive mechanism, that is, extraction of blood, which is a time-consuming technique and can also expose medical personnel to blood-transmissible diseases or infection and results in greater labor and cost of equipment. A hybrid three-layer convolutional neural network architecture is utilized to detect anemia using conjunctiva images. Three single convolutional neural network architectures, that is EfficientNetV2B0, MobileNetV3Small and ResNet50V2 are integrated to develop a three-layer hybrid CNN architecture (EfficientNetV2B0 + MobileNetV3Small + ResNet50V2) to detect anemia. The model exhibits superior detection performance with enhanced computational efficiency. The results of this study highlight the trade-offs between accuracy, robustness, and computational efficiency in deep learning models for anemia detection, which also provides a significant accuracy, for real-world applicability in clinical settings and deployment. This study emphasizes the transformative role of deep learning in advancing anemia detection, paving the way for scalable, accurate, and computationally efficient tools in modern healthcare applications.

PurposeCreativity fosters innovativeness, competitiveness and sustained success. This study aims to evaluate the mediating role of employee creativity in the nexus of human resource management (HRM) practice and organizational ambidexterity. Design/methodology/approachData were collected from 973 employees of 75 health care institutions comprising 48 (indigenously-owned) and 27 (internationally-owned). The positivist paradigm was adopted. Construct validity and reliability were established using confirmatory factor analysis. The study hypotheses and proposed model were evaluated using structural equation modeling FindingsThe study’s results show that ability-enhancing practices and organizational ambidexterity were positively related. Motivation-enhancing practices and organizational ambidexterity were positively related. Opportunity-enhancing practices and organizational ambidexterity were nonsignificantly related. Employee creativity mediates the ability-enhancing practices and organizational ambidexterity relationship. Similarly, employee creativity mediates the motivation-enhancing practices and organizational ambidexterity relationship. Nonetheless, employee creativity did not mediate the opportunity-enhancing practices and organizational ambidexterity relationship. Research limitations/implicationsThe generalizability of the findings will be constrained due to the research’s health care focus and cross-sectional data. Practical implicationsThe findings of the study would provide valuable insight to stakeholder’s, policy makers and management of health care institutions in developing a supportive work environment that promote the generation of originality and novelty in ideas to ensure institutional competitiveness, competencies and innovativeness. Originality/valueBy evidencing empirically that employee creativity mediates the ability-enhancing practices, motivation-enhancing practices and organizational ambidexterity relationship, the study extends the literature on employee creativity, HRM practices and organizational ambidexterity.

This qualitative study assesses the evolution, transformation, and sustainability issues of Ghana’s Chale Wote Street Art Festival (CWSAF), a prominent street art event in West Africa. Using the Event and Festival Lifecycle Model and Social Exchange Theory as analytical frameworks and semi-structured interviews with twenty purposefully chosen stakeholders, including organizers, traditional leaders, artists, vendors, and community members, the research uncovers a fundamental paradox: CWSAF’s commercial success and worldwide appeal increasingly threaten the grassroots authenticity and community ties that characterized its beginnings. During the controversial move from Jamestown to Osu in 2023, this tension reached a breaking point. Through the stages of conception, launch, expansion, and consolidation, the festival has produced significant economic benefits, including improved artist awareness, higher vendor sales, and cultural promotion, all of which support SDGs 8 and 11. However, there are also enduring challenges, such as inconsistent finance, problems with material acquisition, geographical limitations, and societal conflict around youth acculturation. The 2023 relocation presents complicated stakeholder dynamics, even if it exemplifies adaptive innovation. This study closes an empirical gap on Ghana’s well-known street art festival by combining theoretical viewpoints and a variety of stakeholder voices. It also provides evidence-based suggestions for planners and policymakers to strike a balance between long-term community sustainability, cultural authenticity, and economic viability.

Abstract This study presents a systematic review of artificial intelligence applications, particularly machine learning and deep learning models, for okra leaf disease detection and diagnosis. Due to the limited number of okra-specific studies, a related study on leaf diseases of other crops was included for comparative analysis. Following the PRISMA framework, 28 peer-reviewed articles published between 2015 and 2025 were examined. The results show that convolutional neural networks dominate the current study, accounting for nearly 70% of all reviewed models. Architectures such as MobileNet, ResNet, and InceptionV3 consistently achieved accuracies above 90%. Okra-focused studies reported performance ranging from 87% to 98.63% (mean = 94.5%) but were constrained by small or imbalanced datasets. In contrast, studies on other crops achieved accuracies between 77% and 99.84% (mean = 96%), supported by substantially larger datasets. The review further identifies limited adoption of explainable AI, vision transformers, and federated learning approaches. Key study gaps include dataset scale, environmental integration, real-world validation, and reproducibility. The study recommends developing large-scale okra-specific datasets, integrating agronomic variables, deploying lightweight, mobile-ready architectures, and strengthening the adoption of explainable and federated learning frameworks to enable scalable, field-deployable diagnostic systems.

Entrepreneurship education (EE) is a key economic driver aimed at stimulating economic growth, technology, innovation and business in Ghana. However, its impact on Business Startup Initiatives (BSIs) remains inadequately explored within Ghanaian Technical Universities (GTUs). In this paper, we investigated the influence of EE and Entrepreneurship courses (EC) on BSIs among business students in GTUs using the social cognitive career theory (SCCT). A survey method was used to collect data from a random sample of 320 business students in the TUs. The PLS-SEM path analysis on EE and EC had a significant positive influence on BSIs in idea generation, business plans, career choices, goals, and innovation. This study establishes the importance of EE and EC in enhancing BSIs among TU business students. This research encourages the TU management to focus on EE and the EC approaches in initiating startups. The SCCT used contributed significantly to the BSI dimensions.