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Item type:Publication, Deep Learning-Based Digital, Hyperspectral, and Near-Infrared (NIR) Imaging for Process-Level Quality Control in Ecuador’s Agri-Food Industry: An ISO-Aligned FrameworkEnsuring consistent quality and safety in agri-food processing is a strategic priority for firms seeking compliance with international standards such as ISO 9001 and ISO 22000. Traditional inspection practices in Ecuador’s food industry remain largely destructive, labor-intensive, and subjective, limiting real-time decision-making. This study developed a non-destructive, ISO-aligned framework for process-level quality control by integrating digital (RGB) imaging for surface-level inspection, hyperspectral imaging (HSI) for internal-quality prediction (e.g., moisture, firmness, and freshness), near-infrared spectroscopy (NIRS) for compositional and authenticity analysis, and deep learning (DL) models for automated classification of ripeness, maturity, and defects. Experimental results across four flagship commodities—bananas, cacao, coffee, and shrimp—achieved classification accuracies above 88% and ROC AUC values exceeding 0.90, confirming the robustness of AI-driven, multimodal (RGB–HSI–NIRS) inspection under semi-industrial conveyor conditions. Beyond technological performance, the findings demonstrate that digital inspection reinforces ISO principles of evidence-based decision-making, conformity verification, and traceability, thereby operationalizing the Plan–Do–Check–Act (PDCA) cycle at digital speed. The study contributes theoretically by advancing the conceptualization of Quality 4.0 as a socio-technical transformation that embeds AI-driven sensing and analytics within management standards, and practically by providing a roadmap for Ecuadorian SMEs to strengthen export competitiveness through automated, real-time, and auditable quality assurance. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Entropy-Based Assessment of AI Adoption Patterns in Micro and Small Enterprises: Insights into Strategic Decision-Making and Ecosystem Development in Emerging Economies(MDPI AG, 2025-09-05); ; ; This study examines patterns of artificial intelligence (AI) adoption in Ecuadorian micro and small enterprises (MSEs), with an emphasis on functional diversity across value chain activities. Based on a cross-sectional dataset of 781 enterprises and an entropy-based model, it assesses internal variability in AI use and explores its relationship with strategic perception and dynamic capabilities. The findings reveal predominant partial adoption, alongside high functional entropy in sectors such as mining and services, suggesting an ongoing phase of technological experimentation. However, a significant gap emerges between perceived strategic use and actual functional configurations—especially among microenterprises—indicating a misalignment between intent and organizational capacity. Barriers to adoption include limited technical skills, high costs, infrastructure constraints, and cultural resistance, yet over 70% of non-adopters express future adoption intentions. Regional analysis identifies both the Andean Highlands and Coastal regions as “innovative,” although with distinct profiles of digital maturity. While microenterprises focus on accessible tools (e.g., chatbots), small enterprises engage in data analytics and automation. Correlation analyses reveal no significant relationship between functional diversity and strategic value or capability development, underscoring the importance of qualitative organizational factors. While primarily descriptive, the entropy-based approach provides a robust diagnostic baseline that can be complemented by multivariate or qualitative methods to uncover causal mechanisms and strengthen policy implications. The proposed framework offers a replicable and adaptable tool for characterizing AI integration and informing differentiated support policies, with relevance for Ecuador and other emerging economies facing fragmented digital transformation. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Artificial intelligence for the detection of acute myeloid leukemia from microscopic blood images; a systematic review and meta-analysis(Frontiers Media SA, 2025-01-17) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, AQSA—Algorithm for Automatic Quantification of Spheres Derived from Cancer Cells in Microfluidic Devices(MDPI AG, 2024-11-20);