Introduction: Digital media education increasingly requires the integration of theoretical knowledge with practical application to enhance creativity and technical skills. Conventional teaching methods often fail to provide personalized guidance or maintain style consistency, limiting learner engagement and skill development. Objective: To address these challenges, an Artificial Intelligence (AI) driven system was developed to bridge theory and practice, improving learner engagement, creative efficiency, and skill acquisition. Method: The system employs a Novel Artificial Jellyfish Search Optimizer-driven Temporal Residual Gated Recurrent Unit (AJS-TRes-GRU) to optimize automated content generation, ensuring style consistency and adaptive instruction. Input data, including student engagement metrics and digital media content from educational platforms and social media, are preprocessed using image resizing and median filtering. The WikiArt Dataset was utilized for training the system, providing a diverse collection of high-quality artwork to ensure the style-consistent generation of digital media. The TRes-GRU framework automates style-consistent digital media generation, fine-tunes GRU parameters via AJSO, and builds detailed learner profiles. Integration with Creative Intelligence Cloud (CIC) supports hands-on production with personalized guidance. Result: The model achieved an Inception Score (IS) of 5,2, SSIM of 0,9, and FID of 9,17, demonstrating superior performance over prior approaches using python simulation tool; paired t-test results showed narrow 95 % confidence intervals, confirming the model’s reliability and consistency. Overall, the system produces realistic, structurally accurate, and style-consistent digital media, significantly improving content quality, learner engagement, and skill acquisition. Conclusion: By combining AI-driven content generation with adaptive learning frameworks, the research creates an immersive, personalized digital media education environment, effectively preparing learners for evolving industry demands.

Introduction; The detection of motion-based cardiac abnormalities at an early stage proves difficult because individual systems fail to measure how motion affects depth and structural changes. A multimodal spatiotemporal system would enhance the accuracy of medical diagnoses. Objective; The research aims to create a real-time system which unites cardiac video data with range/depth information to detect cardiac conditions at an early stage. Method; The system operates through independent encoders which join their data streams through a gated fusion module. The system performs denoising operations followed by statistical normalization and geometric transformation of the input data. The system uses beat-level temporal attention to identify essential time segments for clinical evaluation. The research evaluated system performance through comparison with video transformers and traditional temporal analysis methods. Result; The model produced F1 reached 0.945 while AUROC reached 0.9978 and the model achieved sensitivity at 0.950 and specificity at 0.940 and precision at 0.940 and AUPRC at 0.972. The system demonstrated excellent calibration performance through its ECE and Brier values which approached perfect results (slope ≈ 1.01, ≈ 0). The system produced useful screening results when using 10% and 20% thresholds which produced 0.142 and 0.118 respectively. The system performed real-time processing at 4.9 GFLOPs while maintaining a processing time of ~98 ms. Conclusion; The combination of intensity dynamics with depth-derived geometry allows for accurate real-time cardiac prediction with precise calibration. The proposed method delivers superior results than single-signal systems and conventional temporal methods which makes it a useful advancement for early detection and point-of-care cardiology.

Human resources face a major challenge in extracting and identifying the semantic correspondence of data, in particular the soft skills most recruiters seek from heterogeneous data. The complexity lies in identifying the relationships between textual descriptions in CVs, keywords, descriptions in professional networks, and relevant soft skills such as communication, persuasion skills, negotiation, relationship building, empathy, teamwork, conflict resolution, emotional intelligence, time management, work ethics, after analysis and research, we chose these soft skills as input data because they encompass all the soft skills that a recruiter might look for in a candidate for any position. The present study introduces a predictive hiring system that assesses candidate performance based on soft skills extracted from three main sources, namely resumes, professional social network profiles, and psychometric assessments. A dataset of over one million candidate records was processed. Data analysis relied on state-of-the-art NLP techniques, including word embeddings and contextual language models, in order to build a semantic database linking keywords, phrases, and descriptions to targeted soft skills. Machine and deep learning models were applied, followed by an ensemble approach integrating KNN, Decision Tree, and Random Forest. To overcome prediction accuracy and overfitting limitations, a meta-model XGBoost was developed, achieving superior results with an accuracy of 98%. The results demonstrate that the proposed meta-model outperforms baseline approaches, delivering high predictive accuracy and robust generalization. These findings highlight the potential of combining semantic analysis with advanced machine learning to support more reliable and scalable predictive recruitment systems.

The failure of information system implantations in small-scale organizations, such as savings and credit cooperatives, remains a recurring challenge in Latin America, primarily due to a lack of awareness and the underutilization of structured frameworks. This article presents two case studies conducted in Ecuadorian Segment 5 cooperatives to analyze their experiences in adopting financial management software using the SImplE framework (Simple Implantation of Enterprise IT Applications in SMEs) as an analytical reference. Although each cooperative carried out the implantation process empirically and under the guidance of its technology provider, the SImplE model enabled a structured analysis across three phases: pre-implantation, implantation, and post-implantation. The research employed a qualitative methodology, based on semi-structured interviews and document analysis. Findings reveal that the absence of formal planning, ad hoc definition of system requirements, empirical software selection, and the lack of written contracts all contributed to the system's long-term sustainability. Nonetheless, the implantation was made possible through institutional commitment and technical support. The study concludes that proper software selection, a well-managed stabilization phase, and the adoption of structured frameworks such as SImplE are critical factors for improving the effectiveness, continuity, and scalability of technology implantation processes in resource-constrained organizations.

The article proposes an approach to selecting a priority public project for financing in the context of a limited budget and lack of unanimity among community residents. Based on the results of an expert survey, a fuzzy decision-making model was built that considers three key variables: the amount that the community is willing to invest, the expected utility of each project, and the decision on the feasibility of increasing or decreasing funding. The Mamdani fuzzy inference system in the MATLAB environment was used to implement the model. Nine basic rules were formed based on linguistic assessments and the results were defuzzied. To build an analytical model of the system's behavior, the method of full rotatable design of the second-order experiment was used, which allowed obtaining a quadratic regression dependence of the output variable on the logarithm of the project cost. The voting results showed the absence of a clear leader among the projects, which further substantiates the feasibility of using the proposed approach. The decision to select the optimal project was made based on minimizing the absolute value of the variable Y, which reflects the degree of deviation from the financial balance. The proposed tool is relevant for the processes of decentralization and development of local self-government, as it allows for formalizing decision-making in situations of limited resources and competition between alternatives.

Introduction: Indonesia faces challenges in strengthening national economic resilience amidst rapid digital transformation. Although micro, small, and medium enterprises (MSMEs) are key drivers of the economy, their performance is hindered by limited digital literacy, inadequate infrastructure, and the need for adaptive regulatory frameworks. A comprehensive integration model is required to address these issues.Objective: This study aims to develop and empirically validate a data-driven Digital Economy Integration Model as a strategic instrument to enhance the resilience and competitiveness of the national economy through MSME digital transformation.Method: A mixed methods design was employed, integrating both quantitative and qualitative approaches. The quantitative phase analyzed survey data from 300 digital MSMEs using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS to evaluate measurement validity and structural relationships. The qualitative phase included thematic analysis of stakeholder interviews to gain deeper insights and reinforce the quantitative findings.Results: The quantitative findings demonstrate that digital literacy (β = 0,45, p < 0,001), infrastructure (β = 0,38, p < 0,001), and adaptive regulation (β = 0,33, p < 0,01) have significant positive effects on MSME economic performance and competitiveness. The qualitative results further highlight the importance of multi-actor synergy and inclusive policy frameworks to support digital economy integration.Conclusion: The study confirms that strengthening national economic resilience requires a holistic model that integrates technological capacity, institutional adaptability, and collaborative governance. The proposed model provides empirical evidence of the importance of aligning digital literacy programs, infrastructure investment, and adaptive regulations. Policy implications include accelerating digital infrastructure development, expanding MSME-focused literacy initiatives, and implementing flexible regulatory mechanisms. Overall, the integrated digital economy model presents a replicable framework for promoting sustainable economic growth through digital transformation.

The metaverse can be generally described as a coherent, persistent and interrelated virtual ecosystem, integrating and combining virtual reality (VR), augmented reality (AR), blockchain, and artificial intelligence (AI) technologies to facilitate real-time social and economic connections. Thus, the implementation of the CSR in the metaverse is a sustainable business strategy perspective that is transformational. Companies can use immersive technologies to provide CSR experiences in new formats- virtual volunteering, tokenized philanthropy, simulations of sustainability with a digital twin, or community hubs around the world. The study acknowledged that Digital transformation is growing at a very high rate in Nigeria, which also opens the opportunity to innovate CSR, although the full-scale implementation of the metaverse is in its infancy. The study fingdings revealed that the CSR in the metaverse is not merely an Internet expansion of conventional CSR but more of a disruptive reconfiguring. Metaverse-based CSR allows interactive, immersive, and decentralized ways of communication, unlike traditional CSR campaigns, which take place in a physical or two-dimensional virtual environment. It was concluded in the study that CSR in the metaverse is a strategic opportunity, as well as an ethical imperative. When properly designed, it may not only allow firms to perform better, but also play a significant role in digital equity and sustainable development in the new virtual economy. It was recommended in the study that firms must make sure that CSR activities in the metaverse are material, open, or consistent with long-term sustainability objectives.

Introduction: The rapid progression of generative AI systems has facilitated the creation of human-like text with remarkable sophistication. Models such as GPT-4, Claude, and Gemini are capable of generating coherent content across a wide range of genres, thereby raising critical concerns regarding the differentiation between machine-generated and human-authored text. This capability presents significant challenges to academic integrity, content authenticity, and the development of reliable detection methodologies.Objective: To evaluate the performance and reliability of current AI-based text detection tools in identifying machine-generated content across different text genres, AI models, and writing styles, establishing a comprehensive benchmark for detection capabilities.Methodology: We systematically evaluated ten commercially available AI detection tools utilizing a curated dataset comprising 150 text samples, expanded from the original 50. This dataset included human-authored texts, both original and translated, as well as AI-generated content from six advanced models (GPT-3.5, GPT-4, Gemini, Bing, Claude, LLaMA2), along with paraphrased variants. Each tool underwent assessment through binary classification, employing metrics such as accuracy, precision, recall, F1 scores, and confusion matrices. Statistical significance was determined using McNemar's test with Bonferroni correction.Results indicate that Content at Scale demonstrated the highest accuracy at 88% (95% CI: 84.2-91.8%), followed by Crossplag at 76% and Copyleaks at 70%. Notably, performance varied significantly across different text categories, with all tools exhibiting reduced accuracy for texts generated by more recent models, such as Claude and LLaMA2. False positive rates ranged from 4% to 32%, which raises concerns regarding their applicability in academic contexts. No tool achieved perfect accuracy, and a performance degradation of 12% was observed with models released subsequent to the initial study design.Conclusions: Current AI text detection tools exhibit moderate to high levels of accuracy; however, they remain imperfect, displaying considerable variability across different AI models and text types. The ongoing challenge of achieving reliable detection, coupled with non-trivial false positive rates, necessitates cautious implementation in high-stakes environments. These tools should serve as a complement to, rather than a replacement for, human judgment in academic and professional contexts.

The data used by companies for decision making are exposed to various risks that can compromise their security. In this context, it is essential to identify tools to detect and manage these risks. Therefore, the main objective of this research was to conduct a bibliometric analysis aimed at assessing the development of scientific literature on the application of cyber resilience in universities using artificial intelligence for proactive threat detection. To achieve this purpose, the Scimago portal, specialized in the analysis of scientific production, was used. The study reviewed the distribution of quartiles using bibliometric indicators such as the H-index, journal impact factor, number of published documents, average citations per document, international scientific collaboration, and citations from public funding entities. Likewise, the number of articles cited in the most relevant journals of each quartile was evaluated in order to assess the importance of scientific innovation in improving proactive threat detection through cyber resilience. The results show that most of the scientific production is concentrated in journals belonging to countries with high technological development, especially in the areas of threat detection and application of artificial intelligence. However, there is a low diffusion of research related to cyber resilience in universities through artificial intelligence. which suggests the need to increase investment in science and technology, considering the high risk of cybersecurity attacks to which these institutions are exposed

The rapid advancement of artificial intelligence has transformed the educational landscape, with tools such as ChatGPT gaining popularity among university students. This study analyses the perception and use of ChatGPT in Ecuadorian higher education, exploring its impact on academic performance and associated ethical concerns. The objective was to examine how students from various universities and majors perceive the usefulness of ChatGPT in their academic activities. A non-probabilistic cross-sectional approach was employed, using a structured survey applied to 256 students from six Ecuadorian universities. The methodology included the use of the K-Modes algorithm to cluster the students' categorical responses, visualised through principal component analysis. The results revealed significant differences in the use of ChatGPT according to gender, university and career. Female and engineering students showed higher adoption. Four distinct groups of users were identified, reflecting diverse perceptions of the usefulness and risks of ChatGPT. The discussion addressed variability in ChatGPT adoption, highlighting factors such as technological access and ethical concerns. A tension was noted between perceived benefits and potential risks, such as over-reliance and plagiarism. In conclusion, the study highlights the importance of understanding students' perceptions of ChatGPT to effectively integrate these tools into higher education, while simultaneously addressing ethical concerns and encouraging responsible use of AI in academia.