José Alfredo Herrera Quispe

Learning Management Systems – LMSs are tools that use Information and Communication Technologies – ICT to implement Virtual Learning Environments – VLEs to support institutions dedicated to the teaching-learning process. Using an LMS in virtual classrooms can improve the quality of educational products and offer a wide range of benefits; however, if not properly planned, implementation can fail. This research presents a Systematic Literature Review – SLR on LMS implementation in Higher Education Institutions – HEIs, based on 33 studies retrieved from six scientific databases. The selected articles respond to three research questions proposed in this paper. First, how is the process of implementing an LMS in HEIs carried out? Second, what are the frameworks for software implementation? Third, what are the critical success and failure factors in implementing an LMS in HEIs? The results highlight the implementation of LMS with emerging technologies and software implementation frameworks, as well as the factors that lead to the failure of LMS implementation in HEIs.

Acute malnutrition affects 3.1% of children under five years of age in Amazonian communities in Peru, where limited access to health services constrains timely nutritional assessment. In this context, this study aimed to develop, implement, and evaluate NutriRadar, a mobile application for automated childhood nutritional classification based on the anthropometric standards of the World Health Organization (WHO). The application was developed using a waterfall software development methodology and implements the calculation of the Weight-for-Height Z-score (WHZ) from basic anthropometric variables (weight, height, age, and sex). NutriRadar was designed with offline functionality, deferred data synchronization, and compatibility with low-end mobile devices to support operational use in Amazonian settings. Field validation was conducted in two early childhood education institutions in Puerto Maldonado, Peru, and included anthropometric assessments of 75 children aged 3–4 years. The application demonstrated stable offline operation, response times suitable for clinical practice, and nutritional classification results equivalent to the WHO Anthro reference tool. NutriRadar represents a viable and reproducible digital automation solution for the operational application of a deterministic WHO anthropometric protocol, contributing to the reduction of operational errors and strengthening standardized nutritional assessment in resource-limited Amazonian contexts.

Robust credit risk prediction in emerging economies increasingly demands the integration of external factors (EFs) beyond borrowers’ control. This study introduces a scenario-based methodology to incorporate EF—namely COVID-19 severity (mortality and confirmed cases), climate anomalies (temperature deviations, weather-induced road blockages), and social unrest—into machine learning (ML) models for credit delinquency prediction. The approach is grounded in a CRISP-DM framework, combining stationarity testing (Dickey–Fuller), causality analysis (Granger), and post hoc explainability (SHAP, LIME), along with performance evaluation via AUC, ACC, KS, and F1 metrics. The empirical analysis uses nearly 8.2 million records compiled from multiple sources, including 367,000 credit operations granted to individuals and microbusiness owners by a regulated Peruvian financial institution (FMOD) between January 2020 and September 2023. These data also include time series of delinquency by economic activity, external factor indicators (e.g., mortality, climate disruptions, and protest events), and their dynamic interactions assessed through Granger causality to evaluate both the intensity and propagation of external shocks. The results confirm that EF inclusion significantly enhances model performance and robustness. Time-lagged mortality (COVID MOV) emerges as the most powerful single predictor of delinquency, while compound crises (climate and unrest) further intensify default risk—particularly in portfolios without public support. Among the evaluated models, CNN and XGB consistently demonstrate superior adaptability, defined as their ability to maintain strong predictive performance across diverse stress scenarios—including pandemic, climate, and unrest contexts—and to dynamically adjust to varying input distributions and portfolio conditions. Post hoc analyses reveal that EF effects dynamically interact with borrower income, indebtedness, and behavioral traits. This study provides a scalable, explainable framework for integrating systemic shocks into credit risk modeling. The findings contribute to more informed, adaptive, and transparent lending decisions in volatile economic contexts, relevant to financial institutions, regulators, and risk practitioners in emerging markets.

The integration of external risk factors (EF) has become increasingly vital for robust credit risk assessment, particularly in emerging economies subject to compounded disruptions. This paper presents a comprehensive, scenario-based methodology for incorporating EF—including pandemic variables (COVID-19 mortality and positive cases), climate anomalies (temperature, road blockages), and social unrest—into machine learning (ML) models. We employ an adapted CRISP-DM workflow, testing stationarity (via Dickey-Fuller), causality (via Granger), and model performance (via AUC and ACC) under scenarios with and without government-backed guarantees. Post-hoc explainability techniques (SHAP, LIME) further reveal which features drive delinquency predictions in EF-enriched contexts. Empirical results, derived from over 8.2 million Peruvian credit records (January 2020–September 2023), confirm that EF integration significantly improves predictive accuracy. Time-lagged mortality (COVID MOV) emerges as the strongest single predictor, while combined crises (pandemic, climate, social unrest) amplify delinquency risk. Models such as XGB, CNN, and XNN exhibit the highest adaptability, consistently attaining statistical significance across different partitions of economic activities. Beyond enhancing model accuracy, EF inclusion highlights key drivers—borrower income stability, debt management, EF severity—thereby supporting more transparent, proactive strategies for financial institutions aiming to mitigate credit defaults in volatile, multi-factor environments.

Cybercrime is a global problem that affects various sectors, particularly the financial sector, where cybercriminals exploit users' lack of security awareness to obtain confidential information through techniques such as phishing, smishing, vishing, and ultimately seizing the OTP code to carry out fraudulent transactions. The objective of this research is to develop the SectraBank model, which incorporates security measures such as the use of the IMEI code, fingerprint authentication, and geolocation to mitigate cyber fraud attacks, specifically in the forms of SIM Swapping and Fake Apps used by cybercriminals to capture the OTP code. To evaluate the effectiveness of the model, simulations of real attacks were conducted on mobile phones of 50 users, divided into control and experimental groups. The results demonstrated that SectraBank mitigated 98% of SIM Swapping attacks and 88% of Fake App attacks. Additionally, 98% of participants expressed their willingness to adopt the model. These results are crucial for the banking sector, as the proposed model offers a robust security strategy that could significantly reduce cyber fraud, enhancing both security and trust in mobile banking applications.

Distribution centers or supply warehouses face significant challenges in the order-picking process. A particular issue is the congestion of operators in the picking zone due to the inaccurate distribution of products in limited locations. To address this issue, "Slotting" process has emerged, aiming to efficiently allocate products to specific locations using analytical methods, enhancing order preparation while concurrently reducing operational costs. However, seasonal variations and fluctuations in trends contribute to oscillations in product demand. Therefore, it is suggested to integrate statistical forecasting methods and machine learning to monitor and anticipate changes of products in picking zones. In this context, this research presents two approaches, the first is to propose a new “Slotting” process model based on the literature review and the second is a comparison between methods such as double and triple exponential smoothing; Seasonal Autoregressive Integrated Moving Average; Long Short-Term Memory and Gated Recurrent Unit were proposed to forecast product demand. To validate the methods, a test was carried out with 50 products and a 7-year historical database from January 2016 to May 2023. This research suggests a hybrid approach in which the methods utilized can be a practical strategy to achieve the best forecasting accuracy, optimizing the Slotting process.

Software development using the agile approach with SCRUM presents itself as an alternative to traditional methodologies, offering incremental, iterative features and continuous code delivery that ensure a product adaptable to changes. However, this approach faces challenges in terms of secure development, as threats and vulnerabilities may not be adequately addressed due to tight deadlines and frequent changes in requirements. In this context, a framework for secure web software development using Agile Methodologies (SCRUM) is proposed. Based on the literature, eight security activities have been selected for this purpose and integrated into the development process, considering their level of agility and cost-benefit. The validity of this proposal was confirmed through an instrument that gathered the opinions of 45 experts in the software development industry in Peru.